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The Smart Grid (SG) paradigm is the next technological leap of the conventional electrical grid, contributing to the protection of the physical environment and providing multiple advantages such as increased reliability, better service quality, as well as efficient utilisation of the existing infrastructure and the renewable energy resources. However, despite the fact that it brings beneficial environmental, economic and social changes, the existence of such a system possesses important security and privacy challenges, since it includes a combination of heterogeneous, co-existing smart and legacy technologies. Based on the rapid evolution of the Cyber-Physical Systems (CPS), both academia and industry have developed appropriate measures for enhancing the security surface of the SG paradigm by, for example, integrating efficient, lightweight encryption and authorisation mechanisms. Nevertheless, these mechanisms may not prevent various security threats, such as Denial of Service (DoS) attacks that target on the availability of the underlying systems. An efficient countermeasure against several cyberattacks is the Intrusion Detection and Prevention System (IDPS). In this paper, we examine the contribution of the Intrusion Detection and Prevention Systems (IDPS) in the SG paradigm, providing an analysis of 37 cases. More detailed, these systems can be considered as a secondary defence mechanism, which enhances the cryptographic processes, by timely detecting or/and preventing potential security violations. For instance, if a cyberattack bypasses the essential encryption and authorisation mechanisms, then the IDPS systems can act as a secondary protection service, informing the system operator for the presence of the specific attack or enabling appropriate preventive countermeasures. The cases we study focused on the Advanced Metering Infrastructure (AMI), Supervisory Control and Data Acquisition (SCADA) systems, substations and synchrophasors. Based on our comparative analysis, the limitations and the shortcomings of the current IDPS systems are identified, while appropriate recommendations are provided for future research efforts.
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Received March 7, 2019, accepted April 2, 2019. Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.
Digital Object Identifier 10.1109/ACCESS.2019.2909807
Securing the Smart Grid: A Comprehensive
Compilation of Intrusion Detection
and Prevention Systems
PANAGIOTIS I. RADOGLOU-GRAMMATIKIS
AND PANAGIOTIS G. SARIGIANNIDIS , (Member, IEEE)
Department of Informatics and Telecommunications Engineering, University of Western Macedonia, Kozani 501 00, Greece
Corresponding author: Panagiotis G. Sarigiannidis (psarigiannidis@uowm.gr)
This work was supported by the European Union’s Horizon 2020 Research and Innovation Programme under Agreement 787011 (SPEAR).
ABSTRACT The smart grid (SG) paradigm is the next technological leap of the conventional electrical
grid, contributing to the protection of the physical environment and providing multiple advantages such as
increased reliability, better service quality, and the efficient utilization of the existing infrastructure and the
renewable energy resources. However, despite the fact that it brings beneficial environmental, economic,
and social changes, the existence of such a system possesses important security and privacy challenges,
since it includes a combination of heterogeneous, co-existing smart, and legacy technologies. Based on the
rapid evolution of the cyber-physical systems (CPS), both academia and industry have developed appropriate
measures for enhancing the security surface of the SG paradigm using, for example, integrating efficient,
lightweight encryption and authorization mechanisms. Nevertheless, these mechanisms may not prevent
various security threats, such as denial of service (DoS) attacks that target on the availability of the underlying
systems. An efficient countermeasure against several cyberattacks is the intrusion detection and prevention
system (IDPS). In this paper, we examine the contribution of the IDPSs in the SG paradigm, providing an
analysis of 37 cases. More detailed, these systems can be considered as a secondary defense mechanism,
which enhances the cryptographic processes, by timely detecting or/and preventing potential security
violations. For instance, if a cyberattack bypasses the essential encryption and authorization mechanisms,
then the IDPS systems can act as a secondary protection service, informing the system operator for the
presence of the specific attack or enabling appropriate preventive countermeasures. The cases we study
focused on the advanced metering infrastructure (AMI), supervisory control and data acquisition (SCADA)
systems, substations, and synchrophasors. Based on our comparative analysis, the limitations and the
shortcomings of the current IDPS systems are identified, whereas appropriate recommendations are provided
for future research efforts.
INDEX TERMS Advanced metering infrastructure, cyberattacks, intrusion detection system, intrusion
prevention system, SCADA, security, smart grid, substation, synchrophasor.
I. INTRODUCTION
The Smart Grid (SG) constitutes a technological evolution
of the traditional electrical grid, by introducing Information
and Communications Technology (ICT) services. The func-
tionality of a typical electrical grid is mainly based on the
energy generation, transmission and distribution processes.
More concretely, it includes power plants, step-up trans-
mission substations, step-down transmission substations,
The associate editor coordinating the review of this manuscript and
approving it for publication was Fangfei Li.
distribution substations and transmission and distribution
lines. On the other hand, as illustrated in Fig. 1[1], SG pro-
vides the required infrastructure and the communication
channels that allow the real-time bidirectional interaction
between the consumers and the utility companies. This com-
munication can provide multiple benefits such as processes
that enable auto metering and maintenance, self-healing, effi-
cient energy management, reliability and security [2]–[6].
However, despite the fact that SG introduces multi-
ple advantages, it also introduces crucial security chal-
lenges, since it combines heterogeneous communications
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This work accessible by IEEE Xplore (DOI: 10.1109/ACCESS.2019.2909807) was published in IEEE Access: The Multidisciplinary Open Access Journal.
P. I. Radoglou-Grammatikis and P. G. Sarigiannidis, “Securing the smart grid: A comprehensive compilation of intrusion detection and prevention systems”, IEEE Access, pp. 1–26, 2019.
P. I. Radoglou-Grammatikis, P. G. Sarigiannidis: Securing the SG: Comprehensive Compilation of IDPSs
FIGURE 1. An abstract architecture model of the SG [1].
networks [7] such as Internet of Things (IoT) [8]–[11]
devices, industrial devices [12], wireless components and
Wireless Sensor Networks (WSNs) [13] characterized by var-
ious security threats [14], [15]. In addition, the integration
of smart devices, such as smart meters, that communicate
with each other without human intervention induces more
security concerns. Furthermore, the necessary existence of
legacy technologies, such as conventional Supervisory Con-
trol and Data Acquisition (SCADA) systems, increase the
potential risks, since these systems may not integrate modern-
ized security solutions. The security breaches in SG mainly
target on the availability, integrity and confidentiality of indi-
vidual entities [14], [15]. In more detail, the different kinds
of Denial of Service (DoS) attacks aim to disrupt the net-
work services and cause significant damages such as a power
outage [16]–[18]. A characteristic example was the cyberat-
tack against a Ukrainian substation resulting in the power
outage for more than 225,000 people [19]. On the other hand,
the false data injection attacks [20]–[23] can modify the data
of smart meters in order to succeed in more economical pric-
ing. Finally, various types of Man in the Middle (MiTM) can
violate the privacy of the systems [24], [25]. Furthermore,
a remarkable and more dangerous category of cyberattacks,
which threatens the SG architecture, is the Advanced Persis-
tent Threat (APT). This term specifies a set of organized and
long duration attacks by security specialists against a partic-
ular target, such as politicians and industries. Examples of
these attacks are Stuxnet [26], Duqu [27], Flame [27], and
Gauss [27].
An Intrusion Detection System (IDS) and even its evo-
lution, the Intrusion Prevention System (IPS), can operate
as a second line of defense in a communication network,
by enhancing the operation of the encryption and authoriza-
tion mechanisms. For instance, if a cyberattack bypasses the
encryption and authorization mechanisms, the IDS or IPS can
timely inform the security administrator or perform appropri-
ate preventive countermeasures. The term Intrusion Detection
and Prevention System (IDPS) will be used from now on
in this paper for referring to both previous terms. In gen-
eral, the rapid progress of computer networks necessitated
the development of appropriate mechanisms that have the
ability to automate the process of detecting or/and preventing
possible security violations. The presence of these systems
in SG is required, since the security policy violations in this
ecosystem may cause dangerous situations and disastrous
accidents. A significant advantage of the specific systems is
that they possess the ability to recognize zero-day attacks by
using artificial intelligence mechanisms. Therefore, in this
paper, we provide an analysis of 37 cases of IDPS systems
devoted to SG, by evaluating and comparing the cyberattacks
that they are able to detect, their methodology, the detec-
tion performance and finally the consumption of computing
resources. Based on this analysis, we specify the limitations
and shortcomings that characterize these systems and provide
research directions for future work.
In particular, the rest of this paper is organized as fol-
lows: Section II discusses the related surveys in the liter-
ature and provides the motivation and contributions of our
study. Sections III and IV introduce an overview of SG and
IDPS systems respectively. Section Vpresents and explains
the requirements that should characterize these systems.
Section VI provides an analysis of 37 IDPS cases, by inves-
tigating their main characteristics. Section VII interprets,
evaluates and compares the results exported from the previous
analysis. Finally, Section VIII provides trends and research
directions concerning the security of SG, focusing on IDPS
systems, while section IX presents the concluding remarks of
this study.
II. MOTIVATION AND CONTRIBUTION
Although SG can provide multiple benefits, like better energy
management and improved reliability, its independent and
interconnected nature generates at the same time critical
cybersecurity vulnerabilities that in turn can lead to a wide
range of consequences such as power outage, brownout,
energy theft, energy consumer privacy breach. In particular,
most of the communication protocols adopted by SG are
characterized by severe security gaps, since do not comprise
authentication and access control mechanisms, thus enabling
possible adversaries to launch various cyber-physical attacks.
Fig. 2depicts a pictorial view of such attacks against SG.
A characteristic example of cyberattacks against a critical
infrastructure was the Stuxnet worm [26], which exploited
four zero-days vulnerabilities. Furthermore, the diversity and
complexity of communications that take place in SG, as well
as the huge volume of data generated by the various subsys-
tems, hinder the adoption of conventional security measures.
Therefore, it is clear that the presence of IDPS systems is vital
for the entire operation of SG and mainly for ensuring the
essential security requirements: Confidentiality, Integrity and
Availability (CIA).
Several studies have examined the security issues in the
SG paradigm, by analyzing security challenges, threats and
corresponding countermeasures. Some of these are listed
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P. I. Radoglou-Grammatikis, P. G. Sarigiannidis: Securing the SG: Comprehensive Compilation of IDPSs
FIGURE 2. SG cyberattacks.
in [8], [14], [15], [28]–[38]. Since that the nature and means
of cyberthreats evolve rapidly, the creation of corresponding
surveys and review papers is quite crucial, as they present
state of the art and identify possible challenges, security gaps
and research directions. Other works follow a more precise
approach, by examining the security issues regarding partic-
ular protocols that are commonly utilized in the SG com-
munications. Concretely, in [39], [40], the authors examined
the security issues of IPv6 over Low-Power Wireless Per-
sonal Area Networks (6LoWPAN) and IEC 61850 [41], [42]
standards respectively. Similarly, in [43] the authors investi-
gate various encryption and authentication protocols for SG.
Nevertheless, only a few studies have examined the contri-
bution of the IDPS systems for the contemporary electrical
grid. Specifically, in [44], the authors provided an exten-
sive study and comparison of multiple IDPSs devoted to
the Cyber-Physical Systems (CPSs), such as SG. Similarly,
in [45], [46] the authors investigated various IDPS instances
concerning the protection of IoT; SG is considered as the
largest use case of IoT [47]. On the contrary to the pre-
vious studies, the papers [48], [49] follow a more specific
approach and examine the IDPS systems devoted to the
protection of the Advanced Metering Infrastructure (AMI).
Finally, the work [50] evaluates three open-source Security
Information and Event Management (SIEM) systems for SG.
In particular, the platforms studied are a) the AlienVault
OSSIM [51], b) the Cyberoam iView [52] and c) the Prelude
SIEM [53]. According to the authors’ evaluation criteria,
AlienVault OSSIM and Prelude SIEM present the best per-
formance.
Based on the previous description, only two studies [48],
[49] focus exclusively on the examination of the IDPS sys-
tems for SG; however they are limited only to protecting
the AMI domain. In the light of the aforementioned results,
this work is motivated by the importance of the security
issues in SG, providing a comprehensive survey of the IDPS
systems which discusses critical topics such as the detec-
tion methodology, limitations, shortcomings and the ongo-
ing security requirements. Moreover, this survey examines
not only IDPSs that monitor and control the AMI compo-
nents, but also SCADA systems, substations and synchropha-
sors. Furthermore, contrary to previous works, we analyze
thoroughly each case, by investigating its architecture, the
detection technique, the kinds of cyberattacks that are
detected, the resources consumption, performance, the uti-
lized datasets and the software packages. In conclusion,
the desired purpose of this paper is to constitute a stopping
point for the interested parties that intend to work with the
IDPS systems for SG. The contribution of our work is sum-
marized in the following sentences:
Identifying the requirements for effective IDPS sys-
tems devoted to protecting the SG components: Since
SG consists of several and heterogeneous technologies,
components and communication interfaces, the conven-
tional IDPS systems (coming from computer networks)
cannot meet the security requirements of SG. In this
paper, we identify these requirements that subsequently
are utilized to evaluate the various relevant IDPS found
in the literature.
Providing a comprehensive and comparative anal-
ysis of IDPS systems devoted to protecting SG: In
particular, we investigate thoroughly 37 IDPSs capable
of detecting cyberattacks against either the entire SG
ecosystem, AMI, SCADA, substations and synchropha-
sors.
Identifying existing weaknesses of the current IDPS
systems for SG: Based on our analysis and taking into
account the requirements of IDPS systems for the SG
paradigm, we identify the weaknesses of the existing
IDPSs found in the literature.
Identifying the appropriate IDPS for the entire SG
ecosystem: Accordingly, based on our analysis and
after identifying the weaknesses of the existing IDPS,
we specify the appropriate IDPS for SG, as well as its
type and attributes.
Determining the current research trends and provid-
ing directions for future work in this field: Finally,
we present the ongoing trends in this field, by identifying
possible directions and technologies for future research
work.
III. SMART GRID PARADIGM
Many organizations such as the Electric Power Research
Institute (ERPI), the Department of Energy (DoE) and the
European Commission Task Force for Smart Grid have been
involved in the definition of the SG paradigm. The term of
SG is defined as the connection of the current electrical grid
with ICT services, by ensuring the corresponding sustain-
ability and allowing the remote control of all processes from
generation to distribution, the bidirectional communication
between consumers and utilities, the distributed production,
storage and smart measurement of electricity. In this section,
we provide an overview of the SG paradigm by analyzing its
components and the corresponding communications.
A. SMART GRID COMPONENTS
The SG paradigm combines various kinds of systems,
technologies and infrastructures such as microgrids, AMI,
substations, synchrophasor systems, SCADA systems and
electric vehicles [14], [54]. From these technologies, AMI
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P. I. Radoglou-Grammatikis, P. G. Sarigiannidis: Securing the SG: Comprehensive Compilation of IDPSs
and SCADA systems are the most critical and vulnerable to
cyberattacks and for this reason, most of the IDPS systems
analyzed below focus on these technologies. Furthermore,
substations and synchrophasor systems are also an attracted
target for cyberattackers, since they are crucial for the normal
functionality of SG. In addition, a remarkable attribute of SG
is its ability to form microgrids whose operation is based on
renewable energy resources. Nevertheless, such microgrids
infrastructures characterized by special features may exhibit
different kinds of vulnerability. Subsequently, we provide a
brief overview of these technologies. More information about
the components of SG is provided in [54].
The AMI provides all operations that are necessitated
for the bidirectional data exchange between the end users
and utility companies. In particular, AMI consists of three
kinds of components: a) smart meters, b) data collectors and
c) AMI headend. Smart meters undertake to monitor the
power consumption and other measurements of the electrical
appliances. Data collectors are responsible for storing the
information provided by multiple smart meters that belong
in a specific geographic area. Finally, the AMI headend is a
central server of the utility company which receives, stores
and manages the information of the data collectors. Based
on the information aggregated on the AMI headend, the util-
ity company is able to take the right decisions concerning
the processes of the electricity generation, transmission and
distribution. It is noteworthy that these components belong
to different geographic areas that can be characterized by
different attributes and constraints. Hence, each of these
areas utilizes appropriate communication technologies that
are determined according to the corresponding attributes.
SCADA systems are part of the industrial environment and
their primary operation is to monitor and control the auto-
mated function of other components. In particular, a SCADA
system consists of a) measuring instruments, b) logic con-
trollers such as a programmable logic controller or a Remote
Terminal Unit (RTU), c) a Master Terminal Unit (MTU)
d) a communication network and e) an HMI. Measuring
instruments refer to sensors that monitor physical measure-
ments such as the temperature, pressure and voltage. Logic
controllers are mainly responsible for collecting data from
the measuring instruments, detecting abnormal behaviors and
activating or deactivating technical components. The logic
controllers interact with MTU which is a central host through
which the system operator can send commands to logic
controllers and receive data. The interaction between MTU
and the logic controllers is realized via the communication
network. This communication network is based on industrial
protocols, such as Modbus [55]–[57] and Distributed Net-
work Protocol 3 (DNP3) [58]. Finally, HMI is a software
package with graphics capabilities installed on MTU and
facilitates the interaction between MTU and logic controllers.
Substations play a significant role in the electrical
grid operation. They participate in the transmission and
distribution operations of the electrical grid. Specifically, they
receive the generated power, configure the distribution func-
tion and control the power increase [54]. They can include
various devices and software components such as Intelligent
Electronic Devices (IEDs), RTUs, HMI and Global Position-
ing System (GPS).
A synchrophasor system constitutes an emerging technol-
ogy which is necessary for the operations of the modern elec-
trical grid. Mainly, it consists of Phasor Measurement Units
(PMUs), Phasor Data Concentrators (PDCs), a communica-
tion network and a Graphical User Interface (GUI) software.
A PMU is a device which executes various measurements
from current/voltage waveforms, such as frequency, phase
angle, active power and reactive power. A PDC undertakes
to aggregate the information of PMUs and transform them
into a single flow. The communication between PMUs and
PDCs is usually carried out through IEEE C37.118.2 and
IEC 61850 [41], [42] standards. Finally, the GUI application
is responsible for visualizing appropriately the various data
from PDCs.
A special characteristic of SG is its ability to form isolated
microgrids that can operate either with the support of the main
electrical grid or independently. Microgrids usually employ
renewable energy resources such as solar energy, wind energy
and hydroelectric energy. At this point, it should be noted that
based on the existing literature we could not find any IDPS
system which focuses on protecting microgrids. This state is
a crucial research challenge in this field, since microgrids
are characterized by different operation features compared
to the main electrical grid that may exhibit various kinds of
vulnerabilities.
B. SMART GRID COMMUNICATIONS
Fig. 3illustrates a generic architecture of SG divided in terms
of communication features. In the first layer, there are three
types of network areas: a) Home Area Networks (HANs),
b) Business Area Networks (BANs) and c) Industry Area
Networks (IANs), characterized by the presence of the con-
sumer. In particular, the main characteristic of these network
areas is the presence of smart meters that monitor the energy
consumption of electronic appliances and transmit them to
the next layer. HAN refers to a network, which includes elec-
tronic and smart devices of a home. The second type, i.e., the
BAN, represents a network, which comprises devices and
technologies required for the functionality of an organization.
Lastly, the IAN identifies a network, which incorporates all
the functional elements required for industry. As illustrated
in Fig. 3, the devices of these networks usually utilize ZigBee
and Z-wave [14], [54]. In rare cases, they also can use IEEE
802.11 (Wi-Fi) or Power Line Communications (PLC).
On the other hand, the second layer refers to the Neighbor
Area Network (NAN) which identifies a small geographic
area of multiple HANs, BANs and IANs. This network com-
prises data collector devices that communicate with smart
meters of the previous networks and aggregate the informa-
tion coming from them. In this kind of network, the respective
devices usually employ IEEE 802.16 (WiMAX - Worldwide
Interoperability for Microwave Access), IEEE 802.11 (WiFi
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P. I. Radoglou-Grammatikis, P. G. Sarigiannidis: Securing the SG: Comprehensive Compilation of IDPSs
FIGURE 3. The SG architecture in terms of communication.
- Wireless Fidelity) standards [14], [54]. Alternatively, they
can also use PLC, satellite, cellular, or Digital Subscriber
Line (DSL) communications.
The third layer is characterized by the Wide Area Net-
works (WANs) that are responsible for connecting multiple
NANs with many other entities such as the AMI headend,
microgrids and transmission networks. This layer aggregates
various information from multiple entities in order to opti-
mize the generation, transmission and distribution processes.
The elements of the particular network can communicate
with each other with various communication types such as
IEEE 802.16, PLC, DSL, satellite, cellular and fibre-optic
communications [14], [54].
Finally, it should be noted that Fig. 3presents a general
architectural schema, from which one or more network areas
can be excluded in some cases. For example, the presence of
NAN can be excluded in some cases where the data collector
is not needed. Nevertheless, the exclusion of NAN does not
exclude the distribution process.
IV. OVERVIEW OF IDPS SYSTEMS
The rapid evolution of the computing systems and the global
utilization of Internet generate new security threats as well as
the need for appropriate security measures such as the IDPS
systems. According to the RFC document 2828, the intrusion
detection process aims at auditing and analyzing security
events in order to identify timely potential malicious activ-
ities. In 1980, the term of IDS was introduced, which can be
considered as a hardware and/or software system automating
the process of monitoring, auditing, analyzing and identifying
possible threats. Specifically, in 1980, James Anderson [59]
inferred that the log files of a computing system can be a very
efficient source for monitoring its state and how the individual
users interact with it. Based on Anderson’s technical report,
researchers started to develop the first IDSs that suitably
analyzed log files for facilitating the security administrators’
work. A remarkable case is Dorothy Denning’s paper [60],
in which she proposed a theoretical IDS model that is based
on an abstract pattern of features. Based on her work, if a
computing system does not meet the features defined, then
it will have probably been affected by a kind of threat. The
next subsections provide an overview of the IDPS systems,
emphasizing the architecture and the detection techniques.
A. ARCHITECTURE OF IDPS SYSTEMS
As illustrated in Fig. 4an IDS usually consists of three main
modules: a) one or more Agents, b) the Analysis Engine
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P. I. Radoglou-Grammatikis, P. G. Sarigiannidis: Securing the SG: Comprehensive Compilation of IDPSs
FIGURE 4. IDS/IPS architecture.
and c) the Response Module. The Agents aim at auditing
and collecting useful information that is preprocessed and
transmitted to the Analysis Engine. Usually, this information
is obtained from the log files and network traffic. The number
of Agents is defined depending on the network topology.
In this context, based on the Agent location, an IDS can be
classified into three categories: a) Host-based IDS (HIDS),
b) Network-based IDS (NIDS) and c) Distributed IDS. The
first type, called HIDS monitors and records only data related
to a single computing system, such as the processes of
the operating system and system calls. NIDS focuses on
the total network traffic, which is exchanged between the
entities of a network, by analyzing attributes and patterns
of the communication protocols. Finally, the Distributed
IDS combines the two aforementioned cases by aggregat-
ing information regarding the total network traffic (case of
NIDS) as well as utilizing appropriate agents, each of which
can monitor a single computing system, as in the case of
HIDS. Next, the Analysis Engine aims at analyzing the
collected information and detecting cyberattack patterns or
possible abnormal behaviors, utilizing specific attack sig-
natures or statistical and artificial intelligence techniques.
Finally, the Response Module informs the system adminis-
trator through alerts and warnings regarding the outcome of
the Analysis Engine. In some cases, the Response Module
may be able to execute specific actions to mitigate automat-
ically the intrusions. In such a case, the system is called
IPS.
B. INTRUSION DETECTION TECHNIQUES
The Analysis Engine utilizes specific techniques to detect
possible threats and anomalies. Mainly, three types of intru-
sion detection techniques are defined: a) Signature-based,
b) Anomaly-based and c) Specification-based. The function-
ality of the first type (Signature-based) is based on matching
the actions that take place in a computing system with a pre-
determined set of intrusion patterns called signatures. If the
characteristics of an action match with one of the signatures,
then a corresponding alert is extracted. It is noteworthy that
this technique requires the knowledge of all vulnerabilities
of the system tested. The use of this technique yields great
reliability with a low rate of false positives, but its weak
point lies in the inability to detect unknown attacks that are
not specified by any signature. As a result, IDPSs utilizing
this method must refresh regularly the set of signatures in
order to include new kinds of attacks. On the other side,
the functionality of the second technique (Anomaly-based)
is based on the determination of the abnormal behaviors as
intrusions. Usually, this method employs statistical analysis
processes or machine learning techniques such as Bayesian
networks, neural networks [61], [62] and Markov models to
detect malicious activities. The use of this technique is more
inaccurate in comparison with the previous one. However,
it has the advantage of recognizing unknown cyberattacks.
Finally, the third technique (Specification-based) utilizes a
set of predetermined rules that define the normal behavior
of the system tested. These rules are called specifications.
If the characteristics of an action differ with one of the spec-
ifications, then a corresponding alert is exported. Therefore,
this method can detect unknown attacks, since it can detect
the possible anomalies. In comparison with the signature-
based approach, this technique is based on the assumption
that if all specifications are applied, the security policy of the
system cannot be compromised. Conversely, the signature-
based technique does not make any such assumption. At this
point, it should be noted that the term ‘hybrid’ is adopted from
now on for characterizing an IDPS that use two or more of the
above techniques.
ACC =TP +TN
TP +TN +FP +FN (1)
Precision =TP
TP +FP (2)
TPR =TP
TP +FN (3)
TNR =TN
TN +FP (4)
FPR =FP
FP +TN =1TNR (5)
FNR =FN
FN +TP =1TPR (6)
V. REQUIREMENTS OF IDPS SYSTEMS
IN THE SMART GRID
The IDPS systems devoted to protecting SG present different
requirements compared to the IDPS of the conventional com-
puter networks. Therefore this section is focused on analyzing
these requirements and the evaluation metrics we adopt for
evaluating and comparing the IDPS cases studied in the next
section. According to the previous IDPS overview, the pri-
mary purpose of an IDPS system is to identify timely indi-
cations of possible intrusions attempts. It would be desirable
that the results of an intrusion detection process can originate
from the value of a binary variable. However, the cyber-
attacks are characterized by more complicated operations
and the information generated by IDPSs is more complex.
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P. I. Radoglou-Grammatikis, P. G. Sarigiannidis: Securing the SG: Comprehensive Compilation of IDPSs
Consequently, we identify the following requirements for
evaluating the performance of the IDPS cases in the next
section.
Detecting a wide range of intrusions: Identifying mali-
cious activities that originate from external unauthorized
users or malicious insiders. It should be highlighted that
the modern IDPSs must include appropriate mechanisms
to deal with zero-day attacks.
Timely intrusion detection: The term ‘timely’ does
not necessarily refer to real-time detection, as this state
introduces significant operational and response issues.
However, it is required to detect an intrusion within a
reasonable time. Thus, the detection latency should be
calculated during the development and testing process
of a modern IDPS.
High detection performance: A number of basic terms
are explained before defining the adopted IDPS perfor-
mance metrics in this work. As True Positive (TP) is con-
sidered as the number of the correct classifications that
detected the cyberattacks as abnormal behavior. On the
other hand, as True Negative (TN) is identified as the
number of correct classifications that recognized non-
malicious activities as normal behavior. Accordingly,
as False Positive (FP) is considered as the number of
incorrect classifications that identified non-malicious
activities as abnormal behavior. Finally, as False Neg-
ative (FN) is deemed as the number of incorrect classifi-
cations that recognized cyberattacks as normal behavior.
On the basis of these terms, many metrics can be calcu-
lated to evaluate the classification performance. Some
of them that are defined by the Equations (1)-(6) are:
Accuracy (ACC), Precision, True Positive Rate (TPR),
False Positive Rate (FPR), True Negative Rate (TNR)
and the False Negative Rate (FNR). It should be noted
that TPR is also called ‘detection rate’, ‘recall’, ‘sensi-
tivity’ or ‘probability of detection’. More detailed, ACC
represents the ratio between the correct predictions and
the total number of samples. ACC is considered as an
efficient metric when there is an equal number of sam-
ples between the predefined classes. For instance, if a
training set is composed of 98% normal behavior sam-
ples and 2% malicious behavior samples, then the train-
ing accuracy of the classification model can easily reach
98%, predicting each case as normal behavior. Con-
versely, if the training set consists of 60% normal behav-
iors samples and 40% malicious behaviors samples, then
the training accuracy may be reduced to 60%. Therefore,
in some cases, ACC can mislead a security operator,
by giving the false sense of achieving high classification
accuracy. Precision is calculated by dividing TP with
the sum of TP and FP. Particularly, Precision expresses
what proportion of samples that are classified as mali-
cious behavior, indeed present a malicious behavior.
Consequently, Precision provides information concern-
ing the performance of the classification with respect to
FP; nevertheless we consider that an intrusion detection
classification in an industrial environment, such as SG
should pay more attention to FN. Accordingly, TPR is
calculated by dividing TP with the sum of TP and FN.
Specifically, this metric measures what proportion of
intrusions that truly present a malicious behavior was
categorized by the classification model as an intrusion.
In contrast to Precision, TPR provides information with
respect to FN. TNR is the fraction between TN and the
sum of TN and FP, indicating the proportion of normal
behaviors that are predicted as normal. Actually, TNR is
the opposite of TPR. In some cases, TNR is also called
as Specificity or Selectivity. FPR or differently Fall-Out
is calculated by dividing FP with the sum of FP and TN.
Actually, FPR is the opposite of TNR, identifying the
proportion of normal behaviors that are detected as intru-
sions. Finally, FNR is the fraction of FN with the sum of
FN and TP. Respectively with the previous case, FNR is
the opposite of TPR, indicating the proportion of intru-
sions that are detected as normal behaviors. Also, it is
worth mentioning that many researchers utilize Receiver
Operating Characteristic (ROC) curves to evaluate the
performance of a classifier. This curve constitutes a
graphical plot between FPR in the x-axis and TPR in
the y-axis. Normally, in order to define the performance
of ROC curve in a numerical value, the Area Under
the Curve (AUC) is calculated. This value refers to the
probability of a classifier to rank a randomly selected
positive event higher than a randomly selected negative
event.
Attentive performance of computing resources: Some
entities in SG, such as the smart meters, are char-
acterized by constrained computing resources. There-
fore, they may not support the computationally expen-
sive operations of the conventional IDPSs. Conse-
quently, the memory, the computational power and the
energy consumption should be taken into considera-
tion during the development and testing process of an
IDPS.
Scalability: SG consists of several technologies and
components that define the corresponding different
communication interfaces. Therefore, an efficient IDPS
for SG should be scalable, having the capability to moni-
tor and interpret these communications, by decoding and
analyzing the corresponding communication protocols
of SG, thus identifying possible cyberattack patterns.
Moreover, it should be capable of aggregating and ana-
lyzing logs from the various SG components.
Resilient against Cyberattacks: An IDPS for SG
should be resilient against cyberattacks, possessing the
capability to prevent various cyberattacks, protect itself
and activate appropriate self-healing mechanisms in case
of emergency. For instance, if a cyberattack cannot be
hindered, an appropriate mechanism should replace the
violated component, thus ensuring the normal operation.
Friendly visual-based user interface: The informa-
tion generated by IDPS (alerts and warnings) should be
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presented appropriately to the SG operator or the secu-
rity administrator.
VI. IDPS SYSTEMS IN THE SMART GRID
It is clear that the IDPS systems devoted to protecting SG
differ substantially from the IDPSs focused on conventional
computer networks. In particular, the multiple interconnected
and at the same time, independent interactions among the
aforementioned SG components require a distributed IDPS
which will be able to monitor and control the network traffic
and syslogs of all subsystems and connections. Moreover,
such an IDPS has to take into account the hybrid nature
of SG which includes both industrial and ICT components.
Specifically, it has to adapt its functionality depending on the
legacy nature and constrained computing capabilities of the
industrial and IoT devices, such as RTUs and smart meters.
Finally, it has to handle and address timely a wide variety of
cyberattacks and possible anomalies due to the heterogeneous
character of SG components.
In this section, we study 37 different cases of IDPSs for SG.
Table 1summarizes these cases cumulatively, while Table 2
compares them by presenting their most significant character-
istics. The comparison of the IDPSs examined is based on the
target system they monitor as well as their detection technique
and performance. The target system can be a) the entire SG
ecosystem, b) AMI, c) SCADA system, d) substation and e)
synchrophasor. In particular, subsection VI-A discusses the
IDPS systems concerning the entire SG ecosystem. Subsec-
tion VI-B presents those IDPSs focusing on AMI. Subsec-
tions VI-C and VI-D are devoted to the IDPSs monitoring
the SCADA systems and substations respectively. Finally,
subsection VI-E focuses on IDPSs regarding synchrophasors.
Since each IDPS is devoted to protecting a specific category
of target systems, we can examine and compare their archi-
tecture, detection technique, the kinds of cyberattacks they
can detect and finally their performance.
A. IDPS SYSTEMS FOR THE ENTIRE SG ECOSYSTEM
As described before, SG consists of multiple and hetero-
geneous communications that may present various security
gaps and vulnerabilities, thereby making it possible to launch
disastrous cyberattacks. Moreover, SG includes components
characterized by constrained resources that hinder the adop-
tion of conventional cybersecurity mechanisms. Thus, it is
clear that the presence of efficient and lightweight IDPS
systems is necessary for the protection of SG. Subsequently,
we investigate per paragraph appropriate IDPS systems capa-
ble of protecting the entire SG ecosystem.
In [63], the authors proposed an IDS for the entire SG
ecosystem, whose functionality is mainly based on three
entities: a) an Ontology Knowledge Base (OKB), b) a Support
Vector Machine (SVM) [64] model and c) a fuzzy risk ana-
lyzer. The system architecture consists of a number of HIDSs
and NIDSs that are allocated to different elements of SG.
In more detail, each NIDS or HIDS includes four function
modules: a) the trust manager, b) the autonomic manager,
TABLE 1. A summarized presentation of the most important features
found in compiling IDPSs for the smart grid paradigm.
c) the knowledge manager and d) the fuzzy risk manager.
The detection of the possible threats is accomplished by
applying an SVM [64] model whose training process lasted
for 30 hours by using a dataset, which includes 3600 records
of attacks. The specific dataset is a part of OKB and includes
a) records from the KDD 1999 dataset [65] and b) simulated
experiments from the authors. It includes multiple types of
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TABLE 2. Summary of 37 IDPSs cases in SG.
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TABLE 2. Summary of 37 IDPSs cases in SG.
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TABLE 2. Summary of 37 IDPSs cases in SG.
attacks, such as DoS attacks, packet splitting attacks, com-
mand insertion attacks, payload mutation attacks, brute force
attacks, duplicate insertion and shellcode mutation attacks.
Next, in order to reduce the FP alarms, the authors utilized
a fuzzy logic technique to determine a risk value for each
element of the SG environment. These values vary from
0 to 1. Finally, OKB is employed to identify the targets
of attacks. An ontology can be characterized as a dictio-
nary which determines the information about an application
domain and the relations between them. By using the Protege
software [66], the particular IDS is connected to the CoreSec
ontology in order to determine the most appropriate option
of OKB. Concerning the evaluation of the proposed system,
the authors argue that AUC approaches 0.99451.
In this article [67], Y. Zhang et al. suggested a distributed
IDS for the entire SG ecosystem, which is called SGDIDS
and is based on the functionality of an Artificial Immune
System (AIS). The particular system consists of individual
IDS modules that cooperate in a hierarchical manner. More
concretely, each HAN, NAN and WAN includes a distinct
IDS which is responsible for monitoring and controlling the
corresponding communications. The HAN IDS is composed
of three units: a) data collector unit, b) AIS classification
model and c) detection results recording unit. On the other
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hand, the NAN IDS receives the results of HAN IDSs and
also utilizes the AIS algorithms. Accordingly, the WAN IDS
obtains the alerts or warnings of the NAN IDSs and utilizes
the same classification algorithms. If a lower layer IDS (e.g.,
HAN IDS) cannot classify some network activities, then the
next higher layer IDS (e.g., NAN IDS) will undertake to
categorize these activities. Each IDS employs the CLONALG
and AIRS2Parallel detection algorithms. However, each type
of the previous IDSs was trained with different samples of
the NSL-KDD dataset [65], [68], [69], since different areas
networks are commonly exposed to different attacks. The
training processes were carried out with the utilization of
the WEKA [70], [71] software package. Finally, the authors
argue that ACC of the CLONALG and AIRS2Parallel algo-
rithms reach 99.7% and 98.7% respectively.
In this work [72], the authors proposed new locally opti-
mum tests and apply them in SG intrusion and fault detec-
tion problems. Considering that the dynamic time behavior
of an examined system can be approached as a discrete-
time linear state-space model, a failure or intrusion can be
recognized by observing a change in specific system param-
eters. In particular, one way to detect such changes is the
utilization of hypothesis testing. For this reason, the authors
develop two locally optimum tests: the Locally Optimum
Unknown Direction (LOUD) and the Locally Optimum Esti-
mated Direction (LOED) tests. Both of them are appropri-
ate for detecting small changes in the examined system.
However, if the change is large, the Generalized Likelihood
Ratio (GLR) test can be applied in this case. Consequently,
in this paper, the combination of the above methods was
proposed, i.e., the LOUD-GLR and the LOED-GLR tests.
The combined test employ LOUD or LOED, if the change
in the system is quite small and then switches to GLR, if the
change looks large. Finally, concerning the evaluation of the
proposed method, the best TPR approaches 95%.
B. IDPS SYSTEMS FOR AMI
AMI constitutes the main novelty of SG which enables a
bidirectional communication between the utility companies
and energy consumers. Nevertheless, although this commu-
nication benefits both directions, it is based on ICT services
and components that may be characterized by severe vulner-
abilities. A characteristic example is the false data injection
attacks against smart meters. Hence, the corresponding intru-
sion detection mechanisms should be adapted appropriately
in order to control AMI components. The following para-
graphs analyze IDPS systems suitable for the AMI protection.
In this article [73], the authors presented a novel intrusion
detection architecture for AMI and evaluated a plethora of
evolving machine learning algorithms by using the Massive
Online Analysis (MOA) software [74]–[76]. In particular,
the proposed architecture consists of three different IDSs,
which can be installed in smart meters, data collectors and
AMI headends respectively. Each IDS includes four com-
ponents: a) the data acceptor module, b) the pre-processing
unit, c) the stream mining module and d) the decision-making
unit. It is worth mentioning that IDSs can either be incor-
porated into the AMI components or can be implemented
as an individual hardware card. Regarding the evaluation of
the evolving machine learning algorithms, the authors uti-
lized the KDD CUP 1999 dataset and an improved version
of this, called NSL-KDD [65], [68], [69] that include multi-
ple types of attacks, such as, DoS, Remote to Local (R2L)
attacks, User to Root (U2R) attacks and probing attacks. Also,
they utilized multiple evaluation measures such as: a) ACC,
b) the size of the classifier in Kilobyte (KB), c) the processing
time of the classifier, d) the consumption rate of the Random
Access Memory (RAM), e) FPR and f) FNR. The MOA
software provides 16 evolving machine learning algorithms,
from which seven were evaluated. These algorithms are a)
Accuracy Updated Ensemble b) Active Classifier, c) Lever-
aging Bagging, d) Limited Attribute Classifier, e) Bagging
using ADWIN, f) Bagging using Adaptive-Size Hoeffding
Tree and g) Single Classifier Drift. Active Classifier and
Single Classifier Drift are proposed for the IDS which con-
trols network activities of smart meters. Correspondingly,
the authors consider that the Leveraging Bagging algorithm is
suitable for the IDS which is responsible for the data collector.
Finally, the Active Classifier algorithm is suggested for the
IDS of the AMI headends.
In [77] R. Vijayanand et al. presented an anomaly-based
IDS which controls the AMI communications. In detail,
the proposed system is integrated into the data collector
and utilizes a Multi-SVM classifier [64]. A Multi-SVM [64]
classifier consists of multiple SVM [64] classifiers that can
detect various types of attacks. More specifically, the authors
employed the ADFA-LD dataset [78], [79] and applied the
mutual information technique to select the most important
features from the particular dataset. The mutual information
technique is a filter feature selection method which is based
on the entropy concept and distinguishes those features that
achieve the best classification ACC. The features that were
selected from ADFA-LD Dataset are a) Source bytes, b)
Destination time to leave (ttl), c) Source mean, d) Desti-
nation mean and e) Ct_state_ttl. The possible attacks that
can be detected utilizing the aforementioned features are a)
exploits, b) DoS attacks, c) fuzzers, d) backdoors, e) worms
and f) generic attacks. Considering the training process of
the proposed model, for each of these attacks, an SVM [64]
classifier was developed by using a different kernel function.
In particular, the polynomial function was employed for DoS
and backdoor attacks; the Gaussian function was utilized for
normal behaviors and generic attacks and the mlp function
was used for worms, fuzzers and exploits. Concerning the
evaluation of the proposed system, ACC exceeds 90%. TPR
and TNR are calculated at 89.2% and 93.4% respectively.
Finally, it is worth mentioning that the training and testing
processes were conducted by using the Matlab software pack-
age.
Li et al. [80] introduced an intrusion detection method
for AMI, whose operation is mainly based on the Online
Sequence Extreme Learning Machine (OS-ELM) [81].
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OS-ELM is a special feedforward neural network model
which utilizes the online sequence learning for its training
process. More specifically, their methodology consists of
three phases: a) data preprocessing phase, b) initialization
phase and c) online sequence learning phase. In the first
phase, the training data is preprocessed by using the Gain
Ratio Evaluation feature selection method. The second phase
initializes randomly the parameters for the training process
of the neural network. Finally, the third method constitutes
the training process. The dataset that was employed for the
training process can be found on the website [82]. However,
it is highlighted that the specific dataset does not include
network records that identify cyberattacks nor abnormal
behavior patterns. Regarding the evaluation process, multi-
ple experiments were conducted in order to determine the
appropriate parameters for the proposed model. Moreover,
the authors evaluated their model with other classification
algorithms. They claim that their solution overtakes the other
algorithms and ACC approaches 97.239%. Accordingly, FPR
and FNR are calculated at 5.897 and 3.614 respectively.
This article [83] describes an anomaly-based intrusion
detection method which focuses on the false data injection
attacks. In particular, the proposed method is based on a
spatiotemporal evaluation, which controls the correlations
between the state estimations of AMI. As state estimations
are considered various actions such as, energy supply/demand
and electricity pricing. In more detail, the specific method can
mainly be divided into two phases. The first method creates
a set of state estimations which is characterized by spatial
correlations and temporal consistencies. The second method
applies a voting system which classifies each state estimation
into three categories: a) good, b) abnormal and c) unknown.
Concerning the evaluation of the proposed method, two false
data injection attacks were simulated. The target of the first
attack was to maximize the energy transmission costs, while
the second attack intended to cause a power outage. The
authors declare that for the first attack, their method does not
generate any FP. On the other hand, the second attack results
0.43% FPR.
Boumkheld et al. [84] developed an IDS which exclusively
focuses on blackhole attacks. The specific kind of attacks
constitutes a DoS attack which aims to drop all network
packets by advertising malicious nodes or malicious paths.
More concretely, their system controls the communications
of an AMI NAN. To simulate the specific kind of attack, they
utilized the Network Simulator 2 (NS2) [85] simulator and
examined the AMI network as an ad-hoc network by using the
Ad-Hoc On-Demand Distance Vector (AODV) protocol [86].
In more detail, their simulation includes 100 smart meters
nodes, 1 data collector and 2 malicious nodes. The IDS can be
considered as a different node that communicates only with
the data collector node. In order to detect the possible black-
hole attacks, the authors applied the Naive Bayes Classifier
which is based on the Bayes theorem. The features that were
used as input in the Naive Bayes Classifier are a) the number
of route request packets, b) the number of route reply packets
and c) the number of dropped packets. Finally, to evaluate
their IDS they used the Waikato Environment for Knowledge
Analysis (WEKA) [70], [71] software. The authors claim that
their system recorded 100%TPR, 99%ACC, 66% Precision
and AUC approaches 1.
I. Ullah and H. Mahmoud in [87] presented an intru-
sion detection framework for AMI, which also applies
the anomaly detection technique. The architecture of the
proposed system is composed of individual IDS modules
that are placed in different locations in HANs, NAN and
WAN correspondingly. If an IDS module detects a possi-
ble threat, then a related notification will be sent to the
system administrator of AMI. Also, there is a central IDS
module which aggregates and examines further the alarms
generated by the various IDS modules. The authors utilized
the ISCX2012 dataset [88], [89] and the WEKA [70], [71]
software in order to evaluate a plethora of machine learning
classification algorithms. The particular dataset includes var-
ious network attacks that are classified into four categories:
DoS, LAN to LAN (L2L), Secure Shell (SSH) and Botnet.
They evaluated 20 algorithms of which the most efficient
are: J48 [90], JRip, BayesNet, SVM [64] and MLP. The most
efficient algorithm was J48 [90] which achieved 99.70% Pre-
cision and 99.60% TPR.
In this work [91], the authors suggested a flow-based dis-
tributed IDS for AMI, based on the clustering technique. The
proposed system is composed of multiple IDS units that are
installed on the data collectors and the AMI headend. Initially,
the IDS units of the data collectors monitor and analyze
the network traffic, which is exchanged between the data
collectors and smart meters. Subsequently, they detect the
potential abnormal flows and send a summary report of them
to the IDS unit of the AMI headend. The latter undertakes
to investigate further the specific anomalies. The detection
process is based on the Mini-Batch K-Means algorithm and
a sliding window technique. For the training procedure of
the Mini-Batch K-Means clustering algorithm, the authors
created their own dataset which consists of the Transmission
Control Protocol/Internet Protocol (TCP/IP) network flows
features. Also, it is worth mentioning that they utilized the
Principal Component Analysis (PCA) technique in order to
reduce the dimensionality of the dataset. Finally, the num-
ber of clusters (k) was specified at 4, as the specific value
achieved the best silhouette score and FPR. In order to
evaluate the performance of their model, the authors simu-
lated 3 attack scenarios: a) TCP SYN Flooding DoS attacks,
b) stealth port scanning attacks and c) a combination of the
previous ones.
Gulisano et al. [92] introduced a two-tier IDS which
controls the activities that take place on AMI. More con-
cretely, their framework monitors and attempts to detect
timely possible attack patterns by analyzing the network
traffic features between the communications of the data
collectors and smart meters. In order to detect timely the
potential threats, the authors adopted the data streaming
technique [93], in which the analysis of the communication
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traffic is carried out by using acyclic directed graphs. In more
detail, their system consists of two modules called Device
Modeler and Pattern Matcher respectively. The first module
undertakes to monitor the communication traffic and detect
attack behaviors utilizing a Bayesian Network. Specifically,
it monitors the number of requests from the data collec-
tors, the hour and the ID of smart meters. On the other
hand, the second module receives the corresponding alerts
and implements a secondary analysis with the support of a
cybersecurity specialist. In order to evaluate their system,
they simulated energy exfiltration attacks, by introducing
incorrect consumption measurements. They report that TPR
approaches 91%.
In [94], the authors developed an IDS for AMI, in which
the communications are based on the ANSI C12.22 [95]
protocol. More specifically, the proposed system utilizes a
specification-based model which consists of four modules
that were developed by using the Python programming lan-
guage. The first module is called dissector and its work is to
capture the network traffic. The second module called parser
analyzes the network traffic by using specific patterns. The
third module applies determined specifications that define
the normal behavior of a device. Finally, the last module
monitors the operational state of the devices that can be
characterized by three types: a) ‘in-use’, b) ‘off-line’ and c)
‘to configure’. The security specifications were determined
by combining a specific threat model and a system model
based on [96]. In more detail, these specifications are clas-
sified into three categories: network-based, device-based and
application-based. In order to evaluate the IDS, the authors
utilized virtual machines as devices and the Table TstBench
software [94] to emulate the ANSI C12.22 protocol. In the
experimental section, they state that the proposed IDS scored
100% and 99.57% TPR and TNR respectively. However,
it is noteworthy, that only two types of attacks (meter read-
ing attacks and service switch attacks) were examined as
abnormal behaviors. Finally, concerning the evaluation of the
computational performance, they utilized 0.3% of the Central
Processing Unit (CPU) of the virtual machines and 10 MB of
memory.
In [97], X.Liu et al. present a specification-based IDS
which has been specially designed for the smart meter’s com-
munications. Particularly, first, they introduce a modeling
process which describes the information exchange among
the components of a smart meter based on a colored Petri
net. Based on this process, they introduce a threat model
which includes two classes of attacks: a) attacks on data and
b) attacks on commands. Finally, they propose an IDS for
detecting false data injection attacks accomplished via the
access of the smart meter’s physical memory. The architec-
ture of the proposed IDS consists of three elements: a) Secret
Information, b) Event Log and c) Spying Domain. Secret
Information is a confidential data structure which is acces-
sible only for the legitimate procedures and also it is utilized
to encrypt the Event Log. Event Log is used for storing all
the events that are relevant to the smart meter’s activities.
Spying Domain consists of random storage areas that include
the hash code of Secret Information. Through Event Log,
when a cyberattacker attempt to access the storage units,
an alarm is activated. Concerning the evaluation procedure,
the authors developed a tool which configures appropriately
the physical memory, the spying domain and the possible
storage areas that are affected by the cyberattack. Evaluation
figures indicate the values of TPR according to the different
parameters.
Mitchell and Chen [98] presented a specification-based
IDS which includes individual IDSs for the AMI headend,
data collectors and smart meters. For each of the aforemen-
tioned devices, a particular set of behavior rules have been
identified and transformed into a state machine. Specifically,
the IDS controlling the AMI headend has the ability to
monitor the activities of the other AMI headends and data
collectors. Accordingly, the data collector IDS is able to
control the behavior of the other data collectors and smart
meters. Finally, the third kind of IDSs can only monitor the
other smart meters. The threat model applied by the authors,
includes two kinds of attacks: reckless and random attacks.
The authors argue that their methodology accomplishes 100%
TPR, while FPR does not exceed 0.2% and 6% for reckless
and random attacks respectively. Also, ROC curves are pre-
sented.
In this paper [99], P.Jokar and V.Leung presented a
specification-based IPS for the SG applications that employ
ZigBee-based HANs. In particular, the proposed system
mainly focuses on the network traffic features at the Physi-
cal (PHY) and Medium Access Control (MAC) layers. It con-
sists of agents that monitor the network behavior of various
sensor nodes, while at the same time, it can be used for
prevention actions. Also, a central-IPS undertakes to extract
and analyze particular features of the network traffic, thus
detecting possible attacks. If a potential cyberattack or an
abnormal behavior is detected, then a specific prevention
response will be selected by using the Q-learning method
which is a reinforcement learning technique. It should be
noted that the overall network traffic is controlled by the
central-IPS which constantly communicates with multiple
agents. The set of the specification rules is based on 6 char-
acteristics: a) Datagram of IEEE 802.15.4 [100] and Smart
Energy Profile 2.0 (SEP 2.0) [101] protocols, b) traffic rate,
c) Received Signal Strength (RSS), d) sequence number,
e) Packet Error Rate (PER) and f) node availability. Regard-
ing the evaluation of the proposed system, the authors
carried out a theoretical analysis of six attacks against
IEEE.802.15.4, thereby demonstrating that the proposed
IPS can successfully address these attacks. Specifically,
the attacks examined are: a) radio jamming attacks,
b) replay attacks, c) stenography attacks, d) back-off manip-
ulation attacks, e) DoS against data transmission during the
Contention Free Period (CFP) and f) DoS against Guaran-
teed Time Slot (GTS) requests. Subsequently, the authors
conducted two experiments in order to demonstrate that
their system dynamically selects the appropriate prevention
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activity. The corresponding ROC curves are presented.
Finally, the authors discussed five techniques that can bypass
IDPSs. These techniques are: a) obfuscation, b) fragmenta-
tion, c) protocol violation, d) generating network traffic that
targets on IDPS and e) DoS attacks on IDPS. They argue that
only fragmentation techniques cannot be identified by their
proposed system.
In [102], the authors developed a specification-based IDS
for AMI, which combines temporal and spatial detection
techniques, by using Matlab. In more detailed terms, the pro-
posed system focuses on blackhole and time delay attacks.
The blackhole attack was described previously. On the other
hand, the time delay attacks aim at introducing additional
delay time when the packets are transmitted. In particular,
their methodology monitors the number of the transmitted
packets and the transmission delay time between these pack-
ets by using specific numerical intervals that were calculated
by using the mean value and the standard deviation of the
normal distribution. Concerning the evaluation of the pro-
posed model, the authors compared their algorithm only with
the spatial-based, the temporal-based detection technique and
with the development of an SVM [64] model. They report that
the SVM [64] model achieves the best TPR, but their model
achieves the best FPR and the second best TPR. Specifically,
TPR and FPR approach 90% and 6% respectively.
C. IDPS SYSTEMS FOR SCADA SYSTEMS
The safe operation of SCADA systems is crucial for the entire
functionality of critical infrastructures, such as SG. These
systems enable operators to monitor, control and automate the
actions that take place in an industrial environment. However,
their communications are based on insecure protocols, such
as Modbus [55]–[57] and DNP3 [58] that do not integrate
authentication and access control mechanisms, thus enabling
MiTM attacks. Hence, the IDPS systems that are responsible
for protecting SG, should necessarily take into account the
security weaknesses of SCADA communications. Below we
analyze per paragraph appropriate IDPS systems devoted to
protecting SCADA systems.
In [103], T.H. Morris et al. focus their attention on the
Modbus [55]–[57] protocol, providing a set of signature
rules. Modbus is a master-slave, industrial protocol, which
was released by Gould Modicon (now Schneider Electric)
in 1979 for the communication between MTU (master) and
logic controllers (slave). MTU sends a specific query to the
logic controller and subsequently the second transmits its
response to MTU. More specifically, the authors introduce
50 signature rules that concern the Modbus/TCP as well as
the Modbus protocol over a serial communication interface.
The Snort [104]–[106] IDS was utilized for testing these
rules; however, the paper describes these rules in a generic
format, in order to be applied by various IDS systems. Each
rule is defined in a specific text field and is accompanied
with specific details that concern the protocol specifications.
Nevertheless, it is worth mentioning that the authors do not
provide numerical results regarding the effectiveness of these
rules.
In [107], H. Li et al. focus on the DNP3 [58] proto-
col providing appropriate signature rules utilizing the Snort
IDS [104]–[106]. DNP3 is an industrial protocol, which was
standardized by IEC TC-57 and was deployed by IEEE
Electric Power Engineering Association (PES). According
to the authors, the deployment process of DNP3 focused on
the reliability of communications, ignoring the information
security aspects. In particular, DNP3 is characterized by sig-
nificant security deficiencies such as the lack of encryption,
authentication and authorization mechanisms. Therefore, it is
vulnerable to a plethora of cyberattacks such as reconnais-
sance attacks, DoS, protocol anomalies and mixed attacks.
In this work, the authors developed an intrusion detection
template which subsequently was utilized for generating sig-
nature rules for the DNP3 protocol. The signature rules gen-
erated can detect the aforementioned cyberattacks. Moreover,
the authors denote that the specific template can be used for
developing signature rules for other industrial protocols, such
as Modbus [55]–[57] and Profinet. Finally, it is noteworthy
that the authors do not provide any evaluation process.
In [108] E. Hodo et al. present an anomaly-based
IDS for a SCADA simulated environment which utilizes
the IEC 60870-5-104 [109] (IEC-104) protocol. In 1995,
the International Electromechanical Commission (IEC) was
released IEC-60870-5-101 which includes essential telecon-
trol messages between a logic controller and a controlling
server. After six years later, IEC released IEC-104 which
combines the application messages of IEC-101 with TCP/IP.
However, IEC-104 is characterized by several security issues,
since its functionality is based on TCP/IP which itself
presents various vulnerabilities. Moreover, the application
data are exchanged without any authentication mechanism,
i.e., as plaintext. The authors create their own dataset which
includes passive Address Resolution Protocol (ARP) poison-
ing attacks, DoS attacks and replay attacks that replace legit-
imate packets with malicious ones. Based on this dataset and
utilizing WEKA [70], [71], they evaluated multiple machine
learning algorithms, such as Naive Bayes IBk, J48 [90], Ran-
dom Forest [110], OneR, RandomTree and DecisionTable.
J48 [90] and DecisionTable scored the best ACC.
In [111] N. Goldenberg and A. Wool present an anomaly-
based IDS which is devoted to the Modbus/TCP [55]–[57]
communications. More detailed, the functionality of the
specific IDS is based on a Moore Deterministic Finite
Automaton (DFA) which in turn is based on the high peri-
odicity of the Modbus [55]–[57] network traffic. In partic-
ular, the proposed DFA monitors the queries and responses
between MTU and each logic controller, thereby identifying
the normal and abnormal states. More detailed, the DFA
consists of: a) a set of states, b) an alphabet which is a set
of input symbols, c) a transition function and d) the first
state. A state denotes how normal the Modbus [55]–[57]
network traffic is and can take four values: a) Normal,
b) Retransmission, c) Miss and d) Unknown. From the
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aforementioned values, only the Unknown state is considered
as a malicious behavior. On the contrary, the Retransmis-
sion and Miss values denote a benign behavior with some
anomalies. The input symbols and the transition function
determine the states for each communication. The input
symbols are divided into two classes: a) known symbols
and b) unknown symbols. The first category includes those
symbols that were observed during the learning phase and
result in a known state (Normal, Retransmission, Miss), while
the second category implies those symbols that result in the
Unknown state. To evaluate their methodology, the authors
generated two real datasets using Wireshark [112]–[114],
Pcapy [115] and Impacket [116]. Based on the experimental
results, the authors argue that their model did not present any
false alarm.
In [117], S.D. Anton et al. provide a comparison of
four machine learning algorithms concerning the detection
of anomalies in a Modbus/TCP dataset. More specifically,
the authors utilized the dataset of Lemay and Fernandez [118]
which was divided into three sub-datasets, namely DS1,
DS2 and DS3. DS1 consists of 3319 packets and contains
the network traffic between MTU and 6 RTUs, including
75 malicious cases. Similarly, with the same architecture
of one MTU and 6 RTUs, DS2 contains 11166 packets
from which 10 cases are malicious. Finally, DS3 includes
365906 packets with 2016 malicious cases and was generated
by the combination of eight datasets. From these sub-datasets,
specific features were extracted and used for the training
of the machine learning algorithms. It is noteworthy that
the extracted features concern only the TCP/IP stack. The
algorithms evaluated are: a) SVM [64], Random Forest [110],
K-Nearest Neighbor (KNN) [119] and k-means [120]. ACC
of SVM [64] with DS1, DS2 and DS3 is equal to 100%,
100% and 99.99% respectively. Accordingly, ACC of Ran-
dom Forest [110] with DS1, DS2 and DS3 is 100%, 99.99%
and 99.99%. ACC of KNN with DS1, DS2 and DS3 is 99.7%,
99.9% and 99.9%. Finally, ACC of k-means [120] with DS1,
DS2 and DS3 is 98.1%, 55.62% and 63.36%.
In [121], P.H. Wang et al. implement an anomaly-based
IDS utilizing a clustering technique as well as data captured
by a honeypot system. A honeypot [122] is a specific device
or software which intentionally possesses specific vulnera-
bilities in order to attract the cyberattackers. More detailed,
the proposed IDS focuses on detecting intrusions against the
Modbus [55]–[57] protocol, by gathering and using the infor-
mation provided by a Conpot [123] honeypot. Conpot [123]
is a software package which represents a Siemens pro-
grammable logic controller simulating the Modbus proto-
col. During their experiments, the authors considered that
each request to Conpot was a cyberattack. Subsequently,
they combined a similarity evaluation method of the requests
with an agglomerative hierarchical clustering [124] to extract
representative Sequential Attack Patterns (SAPs). After this
process, their system is capable of classifying new requests as
existing SAP or unexpected SAP. Finally, the authors devel-
oped a visualization method which visualizes the flow graphs
of the represented SAPs. Concerning the software packages
utilized by the authors, they are Conpot [123], Python 2.7 and
MongoDB [125], [126]. Based on the evaluation results the
proposed system can detect reconnaissance and DoS attacks
with TPR 90% and 95.12% respectively. FPR of both afore-
mentioned attacks is calculated at 0%.
In [127], Y. Yang et al. provide a specification-based IDS
for the IEC-104 [109] protocol. The core of their system is
named Detection State Machine (DSM) and its functionality
is based on the Finite State Machines (FSM) methodology.
More detailed, the operation of IEC-104 [109] is determined
through the correlations of FSM. In contrast to the tradi-
tional FSM-based systems, their implementation applies a
set of alarms that are capable of distinguishing the protocol
malfunctions. To deploy and demonstrate their methodology,
the authors employ the Internet Traffic and Content Anal-
ysis (ITACA) software [128]. Concerning, the evaluation
results, the authors argue that the True Positive Rate (TPR)
and False Positive Rate (FPR) of their IDS are calculated at
100% and 0% respectively.
In [129], Y. Yang et al. provide signature and specifi-
cation rules for the IEC-104 [109] protocol, by using the
Snort IDS [104]–[106]. After studying the security issues of
the specific protocol, the authors deployed attack signatures
and specification rules for the following attacks: a) unau-
thorized read commands, b) unauthorized reset commands,
c) unauthorized remote control and adjustment commands,
d) spontaneous packets storm, e) unauthorized interrogation
commands, f) buffer overflows, g) unauthorized broadcast
requests and h) IEC-104 port communication. Concerning the
evaluation process, 364 packets were examined from which
41 packets were malicious. Based on the experimental results,
all malicious packets were detected with zero FPs.
In [130], Z.Feng et al. focus their attention on the security
of the Profinet [131], [132] protocol by deploying effective
signature and specification rules utilizing Snort [104]–[106].
Profinet is an industrial standard which was standardized by
IEC 61158 and IEC 61784 and was developed by Profibus
& Profinet International. According to the authors, Profinet
suffers from severe security issues, since it does not inte-
grate encryption, authentication and authorization mecha-
nisms, thus making possible the accomplishment of MiTM
attacks. In this paper, the authors enhance the potential of
Snort [104]–[106] by decoding the Profinet attributes as well
as deploying appropriate signatures for detecting MiTM,
DoS and reconnaissance attacks. Moreover, the authors
deployed specification rules for identifying possible anoma-
lies. To evaluate their work, the authors utilize the traffic
package of [133] and also they create a DoS attack scenario
based on [134]. According to the evaluation process, the pro-
posed signature and specification rules can detect intrusions
against Profinet.
In [135], S. C. LI et al. implement an anomaly-based
IDS for the Modbus protocol, adopting classification data
mining models. In particular, they developed a J48 deci-
sion tree as well as three neural networks, utilizing WEKA.
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To train the above models, they create a dataset by con-
structing a real testbed consisting of a programmable logic
controller, MTU, a cyberattacker unit and a cyberdefender
unit. This dataset includes a) reconnaissance attacks, b)
response injection attacks, c) command injection attacks
and d) DoS attacks. To create their dataset, the authors
utilized Wireshark [112]–[114] as well as a PHP script to
convert the Packet Description Markup Language (PDML)
format of Wireshark [112]–[114] to Comma-Separated Val-
ues (CSV) format. Since their dataset includes very few mali-
cious records, the authors utilized the zeroR [136] classifier.
Specifically, 92.5% of the dataset includes normal records.
Hence, based on zeroR [136], ACC of the data mining models
generated by the authors has to overcome 92.5%. The training
process employed 39 features, but they are not specified by
the paper. Based on the evaluation results, ACC of j48 is
calculated at 99.8361%. Accordingly, ACC of the first, sec-
ond and third neural network is calculated at 97.4185%,
97.4603% and 97.3876%.
D. IDPS SYSTEMS FOR SUBSTATIONS
A substation is a critical location of the electrical grid, where
the electrical energy can be transformed, split and com-
bined. Usually, the operations of contemporary substations
are automated and controlled by a Substation Automated
System (SAS) which incorporates many industrial and ICT
components such as IEDs, RTUs and computers. The com-
munication among these components is based on the IEC
61850 [41], [42] standard which determines the following
goals: 1) interoperability, 2) long term stability and 3) sim-
plified configuration. However, it should be noted that IEC
61850 does not identify any cybersecurity feature for the
safe and normal functionality of SAS. Consequently, possible
cyberattacks can exploit the security gaps of the protocols
defined by this standard, thus making it possible to generate
disastrous consequences. Although IEC 62351 [137] defines
primary security measures, such authentication mechanisms
to secure the protocols defined by IEC 61850, many vendors
and manufacturers do not adopt these solutions. Therefore,
in any case, IDPS is considered as a necessary tool for the pro-
tection of SAS. Each of the following paragraphs describes an
IDPS instance, devoted to protecting substations.
B. Kang et al. in [138] introduced an IDS framework for
substations, which employs signatures and focuses on the
active power limitation attacks. In particular, they developed
a stateful analysis plugin which can be incorporated into
the Suricata IDPS [105], [139], [140]. The specific plugin
includes three functions: a) the application layer protocol
decoder, b) the rule match engine and c) the state manager.
The first function decodes the application layer packets and
extracts their corresponding attributes. The second function
applies content and state inspection rules in order to detect
particular attack patterns. The content inspection rules exam-
ine particular conditions for each application layer packet,
while the state inspection rules check the existence of specific
flags that should characterize the protected devices. Lastly,
the state manager updates the states of the protected devices.
In order to evaluate their framework, the authors applied
their stateful analysis plugin in a scenario which utilizes
the Manufacturing Message Specifications (MMS) [141]
protocol based on the directions of IEC 61850 [41], [42]
standard. They described two attack examples that are
detected successfully, but they do not provide numerical
results.
This work [142] analyzes a specification-based IDS which
is deployed in a substation in South Korea. More specifi-
cally, their IDS is based on the analysis of Generic Object
Oriented Substation Events (GOOSE) [143] and MMS [141]
protocols, examining general network traffic characteristics,
such as the number of bits per second (bps), the number
of packets per second (pps) and the number of connections
per second (cps). For the mentioned characteristics, specific
intrusion detection algorithms were created utilizing statisti-
cal analysis techniques. Details about the architecture of the
IDS are not provided. Regarding the evaluation procedure,
a real dataset was utilized consisting of multiple network
attacks, such as: port scanning attacks, DoS attacks, GOOSE
attacks, MMS attacks, Simple Network Management Proto-
col (SNMP) attacks, Network Time Protocol (NTP) attacks
and ARP attacks. The authors argue that their model scored
100% Precision, 0% FPR, 1.1% FNR and 98.9% TPR.
In [144], Y. Yang et al. provide a specification based
IDPS devoted to protecting substations utilizing the IEC
61850 [41], [42] protocol and particularly the communica-
tions based on MMS, GOOSE and Sampled Measure Value
(SMV). More concretely, the proposed IDPS consists of five
modules: a) configuration module, b) network traffic cap-
ture module, c) process core module, d) rule module and
e) result module. The first one is responsible for examining
the attributes of a specific substation, thus determining them
with specific values and limits. The second undertakes to
capture and isolate the network traffic of MMS, GOOSE and
SMV. The process core module adopts the ITACA software in
order to analyze in detail the attributes of the aforementioned
protocols. The rule module applies the specification rules to
the preprocessed IEC 61850 network traffic. Finally, the last
module informs the security administrator regarding potential
violations. Concerning the specification rules, they can be
classified into four categories: a) access-control detection, b)
protocol whitelisting detection, c) model-based detection and
d) multi-parameter detection. The first one specifies the legit-
imate MAC and IP addresses as well as TCP ports, thereby
forming a whitelist. The rules of the second category detect
as malicious those packets that are not related to IEC 61850.
The next category is devoted to identifying each specification
rule relevant to the attributes of the previous protocols. The
last category includes some rules related to the physical
characteristics of a substation. It is worth mentioning, that all
rules provided by the authors are not identified accurately.
Regarding the evaluation process, data from a real substation
in China was utilized. According to the authors, the proposed
IDS is capable of detecting a plethora of cyberattacks, such as
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DoS, MiTM and packet injection attacks. However, it should
be noted that numerical results are not provided.
In [145], M. Kabir-Querrec et al. introduce a specification-
based IDPS which focuses on IEC 61850 [41], [42] com-
munications of a substation. In particular, the architecture
of their IDPS is based on the data object model defined
by IEC 61850, by introducing a new intrusion detection
function. This data object model consists of many Logical
Nodes (LNs) that satisfy specific functions. All LNs required
for a function form a new logical entity called Logical
Device (LD). A physical device, such as IED can consist of
many LDs. LNs can exchange data among themselves using
a concept named Piece of Information for COMmunica-
tion (PICOM). Although IEC 61850 incorporates a function
for security processes named Generic Security Application
(GSAL), the author deployed a new one which is devoted
to detecting possible anomalies, by determining the normal
specifications of the standard. To define a new function inside
IEC 61850, the following steps have to be accomplished: a) a
formal description of the function is needed, b) the function
has to be decomposed into LNs and c) the interaction with
the other functions has to be determined. Hence, the authors
created an LN called CYSN which is responsible for sniffing
the GOOSE messages and transmitting them to two dedicated
LNs that in turn are devoted to checking the specifications,
thus generating the respective alert in case of a security vio-
lation. More detailed, the first one called CYComChkSingle
undertakes to verify the structure and parameters of each mes-
sage. Accordingly, the second one named CYComChkMany
verifies the consistency of the messages based on a specific
time slot. However, it is worth mentioning that the authors do
not provide detailed information concerning the content and
format of these specifications. In addition, the paper does not
include any evaluation procedure.
H. Yoo and T. Shon in [146] provide an anomaly-based
IDPS for the substations utilizing the IEC 61850 standard.
In particular, the proposed IDPS focus on MMS and GOOSE
protocols, by adopting a one-class SVM classification model,
thus identifying patterns that correspond only to the normal
and legitimate network traffic. More detailed, their IDPS
consists of four processes: a) data capturing and preprocess-
ing, b) outlier processing, c) one-class SVM training and d)
anomaly detection. The first process is devoted to capturing
and preprocessing MSS and GOOSE packets, thus providing
three sets of data. The first set comprises the attributes of each
MMS and GOOSE packet. These attributes are described
in detail in the paper. The second set includes the network
flows formed by MMS and GOOSE communications and
finally, the third one includes traffic information such as
pps and bps. The second process is employed only before
the training of the classification model. It is responsible for
removing the outlier values of the training set, since such
values may denote an anomalous situation. For this process,
the Expectation Maximization (EM) [147] and Local Outlier
Factor (LoF) [148] were utilized through the WEKA soft-
ware. It should be noted that in an industrial environment,
an anomaly may occur even if each component operates
normally. Finally, the last processes focus on training and
testing the one-class SVM classification model respectively.
The training process was implemented by using data from a
real substation. Regarding the evaluation process, FPR ranges
between 1% and 6%.
U. Premaratne et al. in [149] introduce a hybrid
signature-based IDPS for a substation utilizing the IEC
61850 protocol [41], [42]. The proposed IDPS combines sig-
nature and specification rules regarding DoS attacks, traffic
analysis attacks, and password cracking attempts. In par-
ticular, the authors simulated these cyberattacks, thereby
extracting the corresponding signature and specification rules
that in turn were incorporated into Snort [104]–[106]. To sim-
ulate these attacks, they employed the ping command, THC
Hydra [150] and Seringe [151]. Nevertheless, although the
authors argue that their IDPS is devoted to monitoring IEC
61850 packets, it is not able to identify cyberattacks against
IEC 61850 protocols, such as GOOSE and MMS. Moreover,
the authors do not provide numerical results, regarding the
efficiency of their system.
J. Hong et al. in [152] provide a specification-based IDPS
which is also devoted to protecting IEC 61850 [41], [42]
substations, by analyzing multicast GOOSE and SMV mes-
sages. After providing a brief description concerning the
format of GOOSE and SMV protocols, the authors describe in
detail two specification rules that are used to detect possible
GOOSE and SMV cyberattacks respectively. In particular,
concerning the GOOSE cyberattacks, their IDPS can detect
relevant replay attacks, DoS attacks, attacks generating mali-
cious GOOSE data, malicious activities that change GOOSE
control data and finally, actions that modify the time informa-
tion. Accordingly, concerning the SMV attacks, the proposed
IDPS can detect relevant DoS attacks and malicious actions
that modify or generate SMV data. Regarding the architecture
of the proposed IDPS, it consists of four modules: a) packet
filtering module, b) packet parser module, c) specification-
based IDS module and d) HMI module. More detailed,
the first module is responsible for capturing only GOOSE
and SMV packets. Accordingly, the second one undertakes to
extract from the GOOSE and SMV packets the corresponding
attributes. The specification-based IDS module applies the
specification rules and the last module informs the system
operator about possible cyberattacks and anomalies. The
authors tested the effectiveness of their implementation under
real conditions, by constructing a CPS testbed, which in turn
enables the execution of the various cyberattacks. Based on
the authors, FPR can reach 1.61 ×104.
In [153] Yi. Yang et al. have developed a specification-
based IDPS capable of identifying cyberattacks against IEC
61850 [41], [42] substations. Regarding the architecture of
the suggested IDPS, it is composed of the following mod-
ules: a) configuration module, b) network traffic capturing
module, c) IDPS process core, d) rule module and e) result
module. The first module determines the configuration files
that are used to specify the specification rules. The second
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module undertakes to sniff IEC 61850 packets. The following
module analyses the IEC 61850 packets, by extracting their
attributes. The fourth module is responsible for matching the
IEC 61850 packets with a predefined set of specification
rules. Finally, the last module informs the system operator
or the security administrator about the possible intrusions.
Concerning the specification rules adopted by this IDPS,
they can be classified into four categories: a) access control
detection rules, b) protocol-based detection rules, c) anomaly
behavior detection rules and d) multi-parameter detection
rules. The first kind of rules is responsible for allowing
only the network traffic coming from legitimate MAC and
IP addresses. Accordingly, the rules of the second category
undertake to allow only the network traffic specified by the
protocols that are defined by the IEC 6185 standard. The next
rules identify normal behaviors related to the attributes of
the protocols incorporated into IEC 61850. Finally, the last
category identifies some specifications concerning specific
attributes of the physical environment. It should be noted that
the authors do not provide numerical results regarding the
performance of their implementation.
E. IDPS SYSTEMS FOR SYNCHROPHASORS
The modern electrical grids usually are equipped with syn-
chrophasor systems capable of providing real-time informa-
tion concerning electricity measurements, such as current,
voltage and frequency. These systems complement the tra-
ditional SCADA systems, by offering additional wide moni-
toring of the entire electrical grid. Thus the system operator
can identify possible functional problems more quickly, make
better decisions and prevent devastating situations. Although
their role is passive, a successful cyberattack against such sys-
tems can lead to revealing significant information related to
the operation of the electrical grid. In particular, synchropha-
sors usually employ the IEEE C37.118 protocol [154], which
does not integrate any authentication mechanisms, thus mak-
ing it possible to launch MiTM cyberattacks. Therefore, it is
clear that the detection and prevention of cyberattacks against
synchrophasors are crucial. Each of the following paragraphs
analyses an IDPS devoted to protecting such systems.
Pan et al. [155] proposed a hybrid IDS for the synchropha-
sor systems, which combines anomaly-based and signature-
based techniques. In particular, their work is based on the
common-path mining approach and Snort [104]–[106]. They
examined an architecture of three bus two line transmission
system, which consists of a real-time digital simulator, four
relays, four PMUs, a PDC, an energy management system,
which runs the OpenPDC [156], [157] software and a per-
sonal computer which executes Snort [104]–[106]. The input
data are captured by the mentioned entities and are com-
pared with common paths. A common path is a sequence of
system states that may be a specification of normal behav-
ior or a signature of a cyberattack. Based on these char-
acteristics, the particular IDS can classify an activity as:
a) system disturbance, b) normal operation and c) cyber-
attack. The training process of the common-path mining
algorithm includes the creation of a dataset which comprises
25 scenarios of 10000 simulation instances. These scenarios
are classified into three categories, namely a) singe-line-
to-ground faults, b) normal operations and c) cyberattacks.
According to the evaluation results, ACC is calculated at
90.4%.
Khan et al. [158] introduced a hybrid IDS which is
mainly based on specification-based and signature-based
techniques for synchrophasor systems that utilize the IEEE
C37.118 protocol [154]. In more detail, the general archi-
tecture of the proposed system consists of separate HIDSs
and NIDSs called agents and sensors respectively. The agents
monitor the operation of PMUs or PDCs, while the sensors
govern the overall network traffic. Also, there is a manage-
ment server, which aggregates and correlates all information
coming from the individual agents or sensors. In addition,
a database server is responsible for recording any detec-
tion alert or warning. The agents and sensors comprise six
components: a) PCAP filters, b) IEEE C37.118 decoder, c)
analyzer/detector, d) state manager, e) events manager and f)
console. The PCAP filters are developed by using the C/C++
programming language and are responsible for capturing the
IEEE C37.118 packets. The IEEE C37.118 decoder analyzes
the previous sniffing packets and extracts the appropriate
information. The analyzer/detector utilizes a set of rules in
order to detect abnormal behaviors. This set is composed
of four categories rules: a) signature-based rules, b) range-
based rules, c) threshold-based rules and d) stateful behavior-
based rules. According to the authors, the specific set of rules
is able to detect a plethora of cyberattacks, such as, ARP
poisoning attacks, replay attacks, port scanning attacks, DoS
attacks, GPS spoofing attacks, command injection attacks
and physical attacks. Subsequently, the analyzer/detector
communicates with the state manager, which stores possible
alerts or warnings in the database server. Next, the event
manager communicates with the management server, whose
operation was discussed previously. Finally, the console is a
command line or a GUI environment with which the user can
configure the operations of the previous components, e.g.,
the detection rules. For the evaluation process, they employ
the NRL Core software [159], [160]. However, it is worth
mentioning that numerical results are not provided.
Y. Yang et al. in [161] suggest a specification-based IDPS
capable of protecting synchrophasor systems utilizing the
IEEE C37.118 protocol. More specifically, their IDPS con-
sists of three kinds of rules including: a) access control rules,
b) protocol-based rules and c) behavior-based rules. The
access control rules define a whitelist with the legitimate
source and destination MAC and IP addresses as well as
the corresponding ports at the transport layer based on the
Open Systems Interconnection (OSI) model. Accordingly,
the protocol-based rules adopt also a whitelist which in turn
defines the application layer protocols allowed for the interac-
tion among the synchrophasor components. In this case, this
list will enable only the IEEE C37.118 traffic. Finally, the last
category identifies behavior rules based on the attributes of
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the IEEE C37.118 packets, by utilizing a deep packet inspec-
tion process. All rules are described sufficiently in the paper.
Concerning the evaluation process, the authors tested their
IDPS in a real testbed, by executing reconnaissance, MiTM
and DoS cyberattacks. According to the experimental results,
FPR of the proposed IDPS is calculated at 0%.
VII. DISCUSSION
SG consists of a complicated and heterogeneous set of
technologies, including AMI, SCADA systems, substations,
synchrophasors electric vehicles, etc. These technologies
optimize the existing processes of the traditional electrical
grid, but also generate multiple hazards, such as cyberat-
tacks that can cause disastrous consequences, such as a
power outage. In particular, most of the cyberattacks usu-
ally target SCADA systems because they utilize insecure,
legacy communication interfaces and protocols. Character-
istics examples are the Stuxnet worm [26] and the Russian
cyberattack against a Ukrainian substation, resulting in the
power outage for more than 225,000 people [19]. Moreover,
in 2009 Chinese and Russian cyberattackers attempted to
penetrate the US electrical grid, by carrying out reconnais-
sance cyberattacks [169]. Furthermore, in 2014, a campaign
of cyberattacks, named Dragonfly [170] was implemented
against electrical energy infrastructures of many countries,
including the US, Germany, France, Italy, Spain, Poland
and Turkey. The Repository of Industrial Security Incidents
(RISI) [171] comprises 242 reported SCADA cybersecurity
incidents dating from 1982 to 2014. It is clear that the IDPS
systems are an efficient and necessary measure for the protec-
tion of SG, by timely detecting or even preventing the cyber-
security issues. In this work, we present a comprehensive
compilation of 37 IDPS systems, designed for the protection
of SG, including IDPSs that protect the entire SG ecosystem,
AMI, SCADA systems, substations and synchropahsors.
Table 1summarizes the results of our analysis by high-
lighting the most important features found. In particular,
3 IDPSs focus on the entire SG ecosystem, 13 on AMI, 10
on SCADA systems, 8 on substations and 3 on synchropha-
sors. The majority of IDPSs employ the anomaly detection
technique or particular specifications that define the normal
behavior. Concretely, 17 IDPSs employ the anomaly detec-
tion technique, 12 models are characterized as specification-
based, 3 IDPS employ attacks signatures and 5 cases combine
the aforementioned detection methods. Each of these tech-
niques is characterized by advantages and disadvantages. The
signature-based IDPS usually achieves high performance;
however, it is characterized by the inability to detect unknown
threats. Also, generating cyberattack signatures is a very
time-consuming process. On the other hand, the anomaly-
based technique is able to detect zero-day attacks but presents
high FPR. Finally, the specification-based IDPS combines
the advantages of the previous ones; however, in an envi-
ronment, such as SG which includes multiple alterations and
modifications, these specification rules must be redefined
continuously. Therefore, the solution of developing hybrid
IDPSs sounds more promising, since the combination of the
detection techniques can meet the aforementioned issues.
In addition, it is noteworthy that none of the examined
IDPSs include information about the detection latency, while
only two cases [73], [94] comprise information about the
consumption of the computing resources. However, the detec-
tion latency is a significant evaluation measure, especially
in critical systems such as SG, since various cyberattacks
can cause disastrous consequences. Also, the consumption of
the computing resources must be taken into account, given
the establishment of the IoT era, which is characterized by
constrained resource capabilities. Moreover, all IDPS cases
studied are not quite scalable, since they cannot monitor
and interpret data from multiple sources such as the various
communication protocols utilized in SG as well as the logs of
the various components like electricity measurements of HMI
and smart meters. Furthermore, none of the IDPSs examined
does not include self-healing capabilities, providing appro-
priate mechanisms in case of emergency. As mentioned in
Section V, in critical infrastructures, such as SG, recovery
mechanisms, should be activated immediately in emergency
situations, in order to replace the violated components, thus
restoring the normal operation of the system. Finally, it is
worth mentioning that although SG encompasses many com-
plex domains and a huge number of heterogeneous compo-
nents (e.g., smart devices), only one IDPS includes visual-
based mechanisms for facilitating the detection process.
Undoubtedly, the IDPS cases examined before provide
an additional layer for the protection of SG as well as a
valuable effort in this research field. However, none of them
satisfy all requirements defined by Section V. In general,
we consider that the security mechanisms in this domain have
to take into account both the physical and cyber features of
the various components, by adopting situational awareness
processes in a cross-layer approach. Based on Endsley [172],
situational awareness consists of three layers. The first layer
is the perception of information, which identifies the elements
of an environment and their behavior. The second layer is
the comprehension of information received from the previous
layer, comprising storing and interpreting processes. Finally,
the projection level includes predictive and prescriptive algo-
rithms that intend to interpret relevant events. McGuinness
and Foy [173] introduced an additional layer, named Reso-
lution aiming to identify the appropriate practices that opti-
mize a specific situation. Therefore, based on the previous
definitions, we consider that an appropriate IDPS for SG
should apply a hybrid methodology, including signature and
specification rules as well as anomaly detection processes.
Moreover, it should be capable of monitoring and interpret-
ing a set of various SG communication protocols from the
physical layer to the application layer on the basis of the OSI
model, thereby having the capability to detect cyberattack
patterns in a cross-layer approach. Furthermore, it should
analyze logs from the various components, systems and soft-
ware applications, thus being capable of detecting attacks at
the application level. Finally, it should include appropriate
20 VOLUME 7, 2019
P. I. Radoglou-Grammatikis, P. G. Sarigiannidis: Securing the SG: Comprehensive Compilation of IDPSs
self-healing mechanisms that will enable the normal opera-
tion of the entire system, in case of a disastrous cyberattack.
VIII. RESEARCH TRENDS AND DIRECTIONS
It is clear that IDPS systems are critical for any security
system that is deployed in SG. Their role lies in further detect-
ing whether an attacker has compromised grid systems and
gained access to power grid networks. They should be capable
of identifying threats and attacks in the whole SG, by having
global visibility, while being able to access both power and
information systems such as MTU, RTU, PLC, PMU, smart
meters and data concentrators. Moreover, they should be
scalable, by combining various intrusion detection techniques
and monitoring different types of communication and data
such as network traffic, software and system logs as well as
raw data like electricity measurements. Thus, they should be
capable of identifying the type of cyberattacks and activating
the appropriate preventive mechanisms respectively, such as
for example the interruption of a network flow if it is con-
sidered as a DoS attack. Furthermore, IDPSs for SG should
be resilient against those cyberattacks that aim at bypassing
it, by using techniques like for example obfuscation, packet
fragmentation, code packing and encryption, code mutation,
and DoS attacks [99]. Finally, they should provide appro-
priate self-healing mechanisms that will be activated during
emergency situations, by isolating critical parts of SG or
enabling collaborative and redundant mechanisms that in turn
will provide sufficient solutions, until the normal operation is
restored. In this section, we aim at determining the research
trends in this field, also providing specific directions for
future work.
Based on the analysis of Section VII, we have seen that the
existing IDPS are generally unable to interpret the application
layer data for the SG communications, either for a single
packet, or at a session layer, where the state of a connection
should be monitored for inconsistencies [174]. As a result,
most commercial IDPSs do not employ specifications rules,
determining the normal attributes of SCADA and ICS pro-
tocols (e.g., Modbus, IEC 61850 [41], [42], IEC-104 [109]).
Furthermore, traditional approaches cannot be adopted to dis-
criminate between cyberattacks and accidental faults [175].
The Software Defined Network (SDN) technology can offer
significant solutions regarding the previous limitations. The
SDN technology provides global visibility and virtualization
capabilities, thus making possible the generation of speci-
fication rules. More specifically, SDN enables the slice of
the physical communication network into several virtualized
networks devices and deliver traffic belonging to each critical
grid control application. The virtualized network slices a)
inherently enhance security with traffic isolation, b) enable
more fine-grained status monitoring and c) simplify the labor-
intensive protocol vulnerability assessment, i.e., limited to
one particular application per virtual network slice [176].
Therefore, by taking full advantage of the SDN technology,
we consider that the research efforts should focus on devel-
oping SDN-based IDPS systems that will also be capable
of monitoring microgrids. However, based on the existing
literature at this time, we could not find any IDPS devoted
to protecting microgrids.
In the light of the aforementioned remarks, the intercon-
nected and interdependent nature of SG creates new chal-
lenges for the SG security, such as coordinated attacks, APTs,
DoS attacks and botnets. In particular, coordinated attacks
and APTs represent a more dangerous category because they
are sophisticated human-driven attacks against specific tar-
gets. They are usually perpetrated over long periods by groups
of experts that leverage open source intelligence, social engi-
neering techniques and zero-day vulnerabilities. The con-
temporary solutions for the energy sector protection are the
SIEM systems. In particular, SIEM systems deploy multiple
agents in a hierarchical manner to aggregate and normalize
information from different resources, such as security-related
events from end-user devices, servers, network devices and
operating systems [177], [178]. Typically, these systems are
composed of six components/processes which are the source
device, the log collection, the parsing/normalization of the
logs, the rule engine, the log storage and the event monitoring
and retrieval. Moreover, they can integrate specialized secu-
rity mechanisms, such as firewalls, antiviruses, and IDPSs
in order to analyze logs and issue alert notifications or per-
form another response when a threat is detected. However,
the current SIEM systems present three significant limita-
tions regarding the energy sector. Firstly, their functionality
focuses only on the ICT environment without having the
ability to control other infrastructures, such as the industrial
systems. Secondly, even if they can operate in the industrial
sector, usually they utilize corresponding correlation rules
for a few industrial protocols. Finally, the electrical grid is
composed of multiple technological entities that generate a
huge volume of data that cannot be efficiently handled by the
current SIEMs. The adaptation and integration of appropri-
ate host and network IDPS systems inside in a SIEM will
be able to enhance significantly the level of the situational
awareness. Hence, we think that a possible research field in
this domain is the development of a SIEM tool which will
solve the aforementioned limitations, by applying appropriate
IDPS agents. More specifically, this tool should be able to
decode, analyze and correlate various security events pay-
ing attention to the attributes of industrial protocols, such
as IEC 61850 [41], [42], DNP3 [58] and Modbus [55]–[57].
The distributed agents should be able to monitor and control
each device of SG, by implementing a deep packet inspection
process in analyzing each attribute of the corresponding pro-
tocols from the physical to the application layer and based on
specific threshold values should have the ability to identify
possible anomalous behaviors.
Finally, based on the analysis of Section VII, we have
seen that, the IDPS systems should prevent cyberattacks
timely, by applying effective countermeasures, such as self-
healing mechanisms. In contrast to the traditional electrical
grid, SG has the ability to incorporate self-healing mech-
anisms in order to protect itself from natural disasters or
VOLUME 7, 2019 21
P. I. Radoglou-Grammatikis, P. G. Sarigiannidis: Securing the SG: Comprehensive Compilation of IDPSs
cyberattacks. In this field, self-healing entails the division
of the main utility grid into individual microgrids, that can
collaborate with each other in the case of emergency. Based
on recent studies [34], [176], [179], [180], the collaboration
among individual, independent microgrids, called islands,
can enhance the functionality of the entire utility grid,
by increasing its resilience and reliability. In particular, based
on the type of emergency, the self-healing mechanism is
responsible for interconnecting or isolating the corresponding
microgrids. For instance, in the case of a cyberattack, the self-
healing should be able to isolate the compromised systems.
However, it should be highlighted that this countermeasure
reduces the microgrid’s observability (i.e., the capability to
estimate the state of each system), thereby affecting the situa-
tional awareness and other processes. Consequently, by using
the visualization capabilities of SDN, we consider that it is
possible to generate efficient self-healing measures without
reducing the observability of the whole grid, thus providing a
powerful mechanism for critical states.
IX. CONCLUSIONS
SG includes several asynchronous interconnections among
heterogeneous ICT and industrial components that on the one
hand optimize the existing processes of the traditional elec-
trical grid, but also generate multiple hazards. In particular,
the combination of legacy and smart devices as well as the
huge volume of data generated by them hinder the utilization
of conventional security measures. Moreover, the security
gaps of SCADA and SAS protocols like Modbus [55]–[57],
DNP3 [58] and IEC 61850 [41], [42] enable cyberattackers
to launch various attacks, thus endangering confidentiality,
integrity and availability of the entire SG. Hence, an efficient
IDPS system capable of protecting SG communications is
considered as a necessary component of the contemporary
electrical grid.
In this work, we present a comprehensive compilation of
several IDPS systems devoted to protecting SG. In particular,
first, we identify the attributes of SG, by analyzing its main
components, the types of networks and the corresponding
communication technologies. Next, we provide a comprehen-
sive analysis of various IDPS systems, found in the literature
based on specific evaluation requirements that need to be met.
More detailed, we analyze and evaluate 37 IDPS systems by
studying their architecture, intrusion detection methodology
as well as their programming characteristics. Finally, based
on this analysis, we specify the appropriate IDPS for SG and
determine research directions for future work.
In our future work, we intend to address the aforemen-
tioned deficiencies by developing a SIEM system exclusively
for the SG paradigm. The proposed SIEM will be based on
the SDN technology and will integrate big data analytics and
specification-based techniques. More specifically, it will be
able to aggregate, normalize and correlate various security
events as well as decode and analyze multiple industrial and
ICT protocols, thus defining the corresponding specification
and correlation rules.
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PANAGIOTIS I. RADOGLOU-GRAMMATIKIS
received the Diploma degree (five years) from
the Department of Informatics and Telecom-
munications Engineering, University of Western
Macedonia, Greece, in 2016, where he is currently
pursuing the Ph.D. degree. He is also a Research
Associate with the University of Western Mace-
donia in national and European funded research
projects. His main research interests include infor-
mation security, intrusion detection, vulnerability
research, and applied cryptography.
PANAGIOTIS G. SARIGIANNIDIS received the
B.Sc. and Ph.D. degrees in computer science
from the Aristotle University of Thessaloniki,
Thessaloniki, Greece, in 2001 and 2007, respec-
tively. He has been an Assistant Professor with
the Department of Informatics and Telecom-
munications, University of Western Macedonia,
Kozani, Greece, since 2016. He has published over
120 papers in international journals, conferences,
and book chapters. He has been involved in sev-
eral national, EU, and international projects. He is currently a Project
Coordinator of the H2020 Project entitled SPEAR: Secure and PrivatE
smArt gRid (H2020-DS-2016-2017/H2020-DS-SC7-2017). His research
interests include optical and wireless telecommunications, resource alloca-
tion, the Internet of Things, and security and privacy in smart networks.
26 VOLUME 7, 2019
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