SDN-Based Resilient Smart Grid: The SDN-
Panagiotis Radoglou Grammatikis 1, Panagiotis Sarigiannidis 1,* , Christos Dalamagkas 2, Yannis Spyridis 3,
Thomas Lagkas 4, Georgios Efstathopoulos 3, Achilleas Sesis 3, Ignacio Labrador Pavon 5,
Ruben Trapero Burgos 5, Rodrigo Diaz 5, Antonios Sarigiannidis 6, Dimitris Papamartzivanos 7,
Soﬁa Anna Menesidou 7, Giannis Ledakis 7, Achilleas Pasias 8, Thanasis Kotsiopoulos 8, Anastasios Drosou 8,
Orestis Mavropoulos 9, Alba Colet Subirachs 10 , Pol Paradell Sola 10 , José Luis Domínguez-García 10 ,
Marisa Escalante 11 , Molinuevo Martin Alberto 11 , Benito Caracuel 12 , Francisco Ramos 12,
Vasileios Gkioulos 13 , Sokratis Katsikas 13 , Hans Christian Bolstad 14, Dan-Eric Archer 15 , Nikola Paunovic 16,
Ramon Gallart 17 , Theodoros Rokkas 18 and Alicia Arce 19
Citation: Grammatikis, P.R.;
Sarigiannidis, P.; Dalamagkas, C.;
Spyridis, Y.; Lagkas, T.;
Efstathopoulos, G.; Sesis, A.; Pavon,
I.L.; Burgos, R.T.; Diaz, R.; et al.
SDN-Based Resilient Smart Grid: The
Digital 2021,1, 173–187. https://
Academic Editor: Yannis
Received: 27 April 2021
Accepted: 24 September 2021
Published: 30 September 2021
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Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece;
2Testing Research & Standards Center of Public Power Corporation SA, Leontariou 9, Kantza,
15351 Athens, Greece; firstname.lastname@example.org
3Inﬁnity Limited, 2A Heigham Road Imperial Ofﬁces, London E6 2JG, UK; yannis@0inﬁnity.net (Y.S.);
george@0inﬁnity.net (G.E.); achilleas@0inﬁnity.net (A.S.)
4Department of Computer Science, International Hellenic University, 14th km Thessaloniki,
57001 Nea Moudania, Greece; email@example.com
5ATOS Spain SA, Calle De Albarracin 25, 28037 Madrid, Spain; firstname.lastname@example.org (I.L.P.);
email@example.com (R.T.B.); Rodrigo.firstname.lastname@example.org (R.D.)
6Sidroco Holdings Ltd., Petraki Giallourou 22, Ofﬁce 11, Nicosia 1077, Cyprus; email@example.com
7UBITECH Limited, 26 Nikou & Despinas Pattchi, Limassol 3071, Cyprus; firstname.lastname@example.org (D.P.);
email@example.com (S.A.M.); firstname.lastname@example.org (G.L.)
8Center for Research and Technology Hellas, Information Technologies Institute, 6th km Charilaou-Thermi
Road, 57001 Thessaloniki, Greece; email@example.com (A.P.); firstname.lastname@example.org (T.K.); email@example.com (A.D.)
Cyberlens Ltd., 10 12 Mulberry Green Old Harlow, Essex CM17 0ET, UK; firstname.lastname@example.org
10 Fundacio Institut De Recerca De L’Energia De Catalunya (IREC), C/ Jardins De Les Dones De Negre 1,
08930 Sant Adria de Besos, Spain; email@example.com (A.C.S.); firstname.lastname@example.org (P.P.S.);
TECNALIA, Basque Research and Technology Alliance (BRTA), Parque Cientiﬁco Y Tecnologico De Bizkaia,
Astondo Bidea, Ediﬁcio 700, 48160 Derio Bizkaia, Spain; Marisa.Escalante@tecnalia.com (M.E.);
Schneider Electric, Rue Joseph Monier 35, 92500 Ruel Malmaison, France; email@example.com (B.C.);
Department of Information Security and Communication Technology, Norwegian University of Science and
Technology, Hogskoleringen 1, 7491 Trondheim, Norway; firstname.lastname@example.org (V.G.);
14 SINTEF, Sem Saelandsveg 11, 7465 Trondheim, Norway; email@example.com
15 CheckWatt AB, Marketenterivagen 1, 41528 Gotebord, Sweden; firstname.lastname@example.org
16 Realaiz, Mihajla Bogicevica 7, 11000 Beograd, Serbia; email@example.com
17 Estabanell, Calle Rec 26-28, 08400 Granollers, Spain; firstname.lastname@example.org
18 INCITES Consulting, 130 Route d’Arlon, L-8008 Strassen, Luxembourg; email@example.com
19 Control Systems Laboratory, Ayesa, 41092 Seville, Spain; firstname.lastname@example.org
The technological leap of smart technologies and the Internet of Things has advanced the
conventional model of the electrical power and energy systems into a new digital era, widely known
as the Smart Grid. The advent of Smart Grids provides multiple beneﬁts, such as self-monitoring, self-
healing and pervasive control. However, it also raises crucial cybersecurity and privacy concerns that
can lead to devastating consequences, including cascading effects with other critical infrastructures
or even fatal accidents. This paper introduces a novel architecture, which will increase the Smart
Grid resiliency, taking full advantage of the Software-Deﬁned Networking (SDN) technology. The
proposed architecture called SDN-microSENSE architecture consists of three main tiers: (a) Risk
assessment, (b) intrusion detection and correlation and (c) self-healing. The ﬁrst tier is responsible for
Digital 2021,1, 173–187. https://doi.org/10.3390/digital1040013 https://www.mdpi.com/journal/digital
evaluating dynamically the risk level of each Smart Grid asset. The second tier undertakes to detect
and correlate security events and, ﬁnally, the last tier mitigates the potential threats, ensuring in
parallel the normal operation of the Smart Grid. It is noteworthy that all tiers of the SDN-microSENSE
architecture interact with the SDN controller either for detecting or mitigating intrusions.
anomaly detection; blockchain; cybersecurity; energy management; honeypots; intrusion
detection; islanding; privacy; Smart Grid; Software Deﬁned Networking
The evolution of the Industrial Internet of Things (IIoT) is leading the conventional
Electrical Power and Energy Systems (EPES) into a new digital paradigm, widely known
as the Smart Grid (SG). Based on S. Tan et al.’s stufy [
], the SG will compose the biggest
Internet of Things (IoT) application in the near future. Thus, multiple beneﬁts are provided
to both energy consumers and energy utilities, such as many customer choices, pervasive
control, self-monitoring and self-healing. However, this progression also creates severe
cybersecurity and privacy risks that can lead to devastating consequences or even fatal
accidents. It is noteworthy that due to the strict interdependence between the energy sector
and the other critical infrastructures, the EPES/SG cybersecurity incidents can severely
impact the other critical domains. A characteristic cyberattack against the energy sector
was an Advanced Persistent Threat (APT) [
], resulting in a blackout for more than 225,000
people in Ukraine. Similarly, multiple APTs have targeted EPES, such as
TRITON  and Crashoverride .
The vulnerable nature of EPES/SG is mainly related to the legacy Industrial Control
Systems (ICS) and Supervisory Control and Data Acquisition (SCADA) systems. Such sys-
tems utilise insecure communication protocols, such as Modbus , Distributed Network
Protocol 3 (DNP3) [
] and IEC 60870-5-104 [
], that have not been designed with the essen-
tial authentication and authorisation mechanisms. While both academia and industry have
already provided useful security solutions, such as the IEC 62351 standard, unfortunately,
many vendors and manufacturers cannot adopt them, especially in real-time. Moreover, it
is worth mentioning that many challenges arise from the IoT area [
]. In particular, the IoT
inherits the vulnerabilities of the conventional Internet model. Secondly, the vast amount
of the IoT data is an attractive goal for potential cyberattackers.
Therefore, based on the aforementioned remarks, this paper presents the SDN-
microSENSE architecture, which aims to strengthen the EPES/SG resiliency. To this end,
SDN-microSENSE focuses on three tiers: (a) Risk assessment, (b) intrusion detection and
correlation and (c) self-healing. The proposed architecture takes full advantage of the
Software-Deﬁned Networking (SDN) technology in order to recognise, mitigate or even
prevent the potential cyberattacks and anomalies. It should be noted that SDN-microSENSE
is a research Horizon 2020 programme co-founded by the European Union.
The rest of this paper is organised as follows. Section 2discusses similar works.
Section 3presented the proposed architecture, detailing the role of each component. Finally,
Section 5concludes this paper.
2. Related Work
Many papers have examined the cybersecurity and privacy issues of the energy sector.
Some of them are listed in [
]. In particular, in our previous work in [
], we provide
a detailed survey of the Intrusion Detection Systems (IDS) in SG. In [
P. Kumar et al.
present a comprehensive study, detailing the SG cyberattacks and relevant cybersecurity
incidents. Moreover, they introduce a threat model and taxonomy, discussing cyberattacks,
privacy concerns and appropriate solutions. In [
], I. Stellios et al. provide a methodology,
which is utilised in order to evaluate IoT cyberattacks for Critical Infrastructures. Based
on this methodology, they also identify relevant security controls. M. Hassan et al. in 
discuss differential privacy techniques for Cyber-Physical Systems (CPS).
, H. Karim-
ipour et al. present a deep and scalable Machine Learning (ML) system in order to recognise
cyberattacks against large-scale SG environments.
T. Nguyen et al. in 
countermeasures to increase the electrical power grid’s resiliency. In a similar manner,
the authors in [
] analyse and review various works concerning how the SDN technology
can improve the SG security. In [
], A. Musleh et al. provide a survey regarding the
detection of false data injection attacks in the SG. Finally, in [
], the authors provide a
survey about the ﬁrewall systems for SG/EPES. Next, we emphasise on similar works
regarding (a) threat modelling in SG, (b) intrusion detection in SG environments and (c)
mitigating or even preventing cyberattacks through SDN. Each paragraph focuses on a
dedicated case. Finally, based on this brief literature review, we identify how the proposed
architecture is differentiated.
], E. Li et al. introduce a combined method for identifying and evaluating the po-
tential threats against a Distribution Automation System (DAS). In particular, the proposed
method relies on attack trees and the Common Vulnerability Scoring System
in order to specify the possible threats and then to assess them quantitatively. First, the au-
thors introduce the DAS architecture by identifying the functional characteristics and the
security requirements. Next, the authors explain how the CVSS is applied to the attack
tree by calculating the CVSS score for each leaf node and path, thereby calculating the
most threatening path. Next, based on the DAS architecture deﬁned earlier, the authors
specify an attack tree, which considers both the network and physical attacks. Moreover,
the leaf nodes correspond to speciﬁc Common Vulnerabilities and Exposures (CVEs) whose
CVSS score is calculated by the US National Vulnerability Database. Therefore, the CVSS
is applied in the entire attack tree, and the most threatening tree is computed. Finally,
it is worth mentioning that the authors evaluate their method with a similar one, which
relies on the Bayes method and CVSS. Based on this evaluation, ﬁrst, the proposed method
is veriﬁed since both methods compute similar results. Secondly, the proposed method
calculates higher attack probabilities than that using the Bayes methods and CVSS.
], E. Rios et al. present a continuous quantitative risk management methodology
for SG environments. While the paper is focused on SG, the proposed method can be
applied in any Information Technology (IT) and IoT ecosystem. In particular, after dis-
cussing relevant works, the authors explain the proposed methodology, which consists
of ﬁve phases, namely (a) system Attack Defence Tree (ADT) modelling, (b) risk assess-
ment over ADT, (c) risk sensitivity analysis over ADT, (d) risk optimisation of defences
and (e) continuous reﬁnement of risk evaluation. In the ﬁrst phase, the ADT is formed,
by enumerating and structuring the various underlying threats as well as the respective
countermeasures. Next, the second phase calculates (a) the probability, (b) the impact,
(c) the cost and ﬁnally (d) the risk of each ADT node. Next, the risk sensitivity analysis
examines the sensitivity of each ADT node by investigating possible ﬂuctuations in their
values. Subsequently, the risk optimisation of defences intends to optimise the counter-
measures, taking into account possible technical or administrative constraints, such as the
security budget. Finally, the last phase monitors and evaluates continuously the risk-related
values (probability, impact, cost and risk) during the system operation, thus providing the
appropriate feedback. The authors validate the proposed method by carrying out each
phase in a real smart home environment.
The authors in [
] provide an anomaly-based IDS for the IEC 60870-5-104 protocol,
which relies on essential access control and outlier detection. The proposed IDS consists
of two main components: (a) Sensor and (b) server. The sensor is composed of three
modules: (a) Network Trafﬁc Monitoring Module, (b) Network Packet Access Control
Module, and (c) IEC-104 Flow Extraction Module. The Network Trafﬁc Monitoring Module
undertakes to capture the IEC 60870-5-104 network trafﬁc, using a switched port analyser.
The Network Packet Access Control Module adopts a whitelist, which deﬁnes the legitimate
Medium Access Control (MAC), the IP addresses and the 2404 port, which is the default
Transmission Control Protocol (TCP) port for the IEC 60870-5-104 protocol. If the details
of an IEC 60870-5-104 packet (i.e., source/destination addresses and source/destination
ports) do not agree with the whitelist, then an alert is raised. It is worth mentioning that
the alerts are stored in an Elasticsearch database on the server-side. Next, the IEC-104
Flow Extraction Module extracts the TCP/IP network ﬂow statistics used by the outlier
detection mechanism for detecting possible anomalies. On the other hand, the server
Anomaly Detection Module and (b) the Response Module. The Anomaly
Detection Module applies the outlier detection algorithm, which will distinguish whether
an IEC 60870-5-104 is anomalous or not. To this end, three outlier detection algorithms
were tested: (a) One-Class Support Vector Machine (SVM), Local Outlier Factor (LOF)
and Isolation Forest under different ﬂow timeout thresholds: 15 s, 30 s, 60 s and 120 s.
Finally, the Response Module informs the security administrator via Kibana. Based on the
evaluation results, Isolation Forest achieves the best performance when the ﬂow timeout is
deﬁned at 120 s.
In our previous work [
], we developed DIDEROT. DIDEROT is an Intrusion De-
tection and Prevention System (IDPS) capable of detecting and mitigating cyberattacks
against the DNP3. The architectural model of DIDEROT consists of three modules, namely
(a) Data Monitoring Module, (b) DIDEROT Analysis Engine and (c) Response Module.
The Data Monitoring Module undertakes to capture the DNP3 network trafﬁc and gener-
ate bidirectional network ﬂow statistics. To this end,
were utilised respectively. Moreover, the Data Monitoring Module is responsible for nor-
malising these statistics by applying the min-max scaling function. Next, the DIDEROT
Analysis Engine is composed of two ML classiﬁers that operate complimentarily. The ﬁrst
classiﬁer detects particular DNP3 cyberattacks (i.e., multiclass classiﬁcation), including
(a) DNP3 injection
, (b) DNP3 Flooding, (c) DNP3 reconnaissance, (d) DNP3 replay attacks
masquerading. If the ﬁrst classiﬁer classiﬁes a network ﬂow as normal, then
the second classiﬁer is activated to distinguish a possible anomaly (i.e., binary classiﬁca-
tion). The functionality of the ﬁrst classiﬁer relies on a decision tree, while the second
adopts the DIDEROT autoencoder. Finally, based on the outcome of the DIDEROT Analysis
Engine, the Response Module generates security events and informs the
in order to corrupt the malicious network ﬂow. The evaluation results demonstrate the
efﬁcacy of DIDEROT to detect DNP3 cyberattacks.
], P. Manso et al. provide an SDN-based IDPS, which combines the
controller and Snort in order to mitigate DoS attacks. The architectural model consists of
three virtual machines representing (a) the internal network simulated by
, (b) the
SDN-based IDPS and (c) online services. It is noteworthy that the second virtual machine
(i.e., that hosting the SDN-based IDPS) hosts both
. First, Snort receives
the overall network trafﬁc through a port mirroring capability provided by
] of the ﬁrst virtual machine (i.e., Mininet). If Snort detects a potential cyberattack,
based on a UNIX domain socket. Next,
transmits the appropriate
OpenFlow commands to
of the ﬁrst virtual machine (i.e.,
), thus isolating the
malicious nodes. The authors evaluate their IDPS with three distributed denial-of-service
(DDoS) scenarios, measuring (a) DDoS mitigation time, (b) average Round Trip Time and
(c) packet loss. The experimental results demonstrate the efﬁciency of the proposed IDPS.
The authors in  present the SPEAR Security Information and Event Management
(SIEM) system. SPEAR SIEM focuses mainly on EPES/SG environments by detecting
and correlating relevant security events. In particular, SPEAR SIEM is composed of three
architectural layers: (a) Data Capturing layer, (b) Detection Layer and (c) Correlation
Layer. In the ﬁrst layer, SPEAR sensors and the Data Acquisition and Parsing System
are responsible for gathering and pre-processing a variety of data, including (a) network
ﬂow statistics, (b) packet payload information and (c) operational data (i.e., time-series
electricity measurements). Next, the detection layer undertakes to recognise potential
anomalies and cyberattacks. To this end, two components are utilised: (a) Big Data
Analytics Component and (b) Visual-based Intrusion Detection System (VIDS). The ﬁrst is
capable of detecting a plethora of threats by adopting three detection kinds: (a) Network
ﬂow-based detection, (b) packet-based detection and (c) operational data-based detection.
On the other side, VIDS utilises advanced visualisation techniques through which the
security administrator can recognise additional anomalies not detected previously by the
ﬁrst component. Moreover, VIDS operates as the main dashboard of the SPEAR SIEM.
Finally, the last layer is responsible for correlating the security events produced by the
previous layer, thus composing security alerts and updating the trust values of the involved
The authors in [
] present an anomaly-based IDS for EPES/SG. The proposed IDS
uses operational data (i.e., time-series electricity measurements), and its architecture con-
sists of four modules, namely: (a) Data Collection Module, (b) Pre-Processing Module,
(c) Anomaly Detection
Module and (d) Response Module. The ﬁrst module is responsible
for collecting the various operational data. The second module isolates and normalises the
necessary features. Next, the anomaly detection module uses an outlier detection model,
thus recognising possible outliers/anomalies. In particular, six outlier detection methods
are tested: (a) Principal Component Analysis (PCA), (b) OneClassSVM, (c) Isolation Forest,
(d) Angle-Based Outlier Detection (ABOD), (e) Stochastic Outlier Selection (SOS) and
. Finally, based on the detection outcome, the Response Module informs
the user about the presence of potential security events. The main innovation of this work
is the complex data representation during the pre-processing step. The evaluation results
demonstrate the efﬁciency of the proposed IDS.
Admittedly, the previous works present useful methodologies and tools. They focus
mainly on detecting and mitigating potential threats. However, none of them provides an
integrated solution, combining the functional cybersecurity tiers illustrated by
In particular, the proposed solutions do not consider the unique characteristics of EPES/SG
in order to mitigate efﬁciently the various cyberattacks and anomalies. Before the ap-
plication of a mitigation strategy, the corresponding countermeasures should consider
the sensitive nature of EPES/SG. For instance, the isolation of some malicious network
ﬂows corresponding to not critical disturbances can cause more disastrous consequences.
In addition, the above works do not consider emergencies where appropriate measures
should take place in near real-time in order to avoid cascading effects. Finally, the various
solutions have to take into account the quality of the energy grid. Therefore, appropriate
energy-related optimisation methods should take place when a cyberattack or anomaly is
carried out. Based on the aforementioned remarks, SDN-microSENSE aims to provide an
integrated solution that will incorporate detection, mitigation and optimisation systems
into a common platform. This paper focuses on the architecture behind SDN-microSENSE,
detailing the technical speciﬁcations of each component and their interfaces. It is notewor-
thy that due to the complexity of the overall SDN-microSENSE solution and the presence
of multiple components, this paper is devoted only to the SDN-microSENSE architecture
without discussing in detail the technical details for each component and the corresponding
evaluation results. To the best of our knowledge, SDN-microSENSE constitutes the ﬁrst
solution, which integrates and harmonises (a) collaborative risk assessment, (b) intrusion
detection and correlation and (c) self-healing into a common platform. Some individual
works that demonstrate the efﬁciency of the corresponding SDN-microSENSE components
are given in [29–32].
SDN DATAPATH 1
NETWORK ELEMENT 1
SDN DATAPATH 2
NETWORK ELEMENT 2
SDN DATAPATH N
NETWORK ELEMENT N
o o o
DATA / INFRASTRUCTURE PLANE
Load Servers SCADA
Risk Level Assessment
SDN-microSENSE STRUCTURAL (FUNCTIONAL) VIEW
SDN SWITCHES SDN SWITCHES SDN SWITCHES
Figure 1. SDN-microSENSE Architecture—Structural View.
3. SDN-microSENSE Architecture
Figure 2depicts the SDN-microSENSE business logic based on the SDN architectural
model. It comprises three main conceptual frameworks [
], namely (a) SDN-microSENSE
Risk Assessment Framework (S-RAF), (b) Cross-Layer Energy Prevention and Detection
System (XL-EPDS) and (c) SDN-enabled Self-healing Framework (SDN-SELF) that are
deployed throughout the four SDN planes: (a) Data Plane, (b) Control Plane, (c) Application
Plane and (d) Management Plane. The term conceptual framework refers to a set of functions
and relationships within a research area [
]. Therefore, the SDN-microSENSE frameworks
mentioned earlier focus on the following cybersecurity-related research areas: (a) Risk
assessment, (b) intrusion detection and correlation and (d) self healing and recovery. Each
of the SDN-microSENSE frameworks takes full advantage of the SDN technology in order
to detect, mitigate or even prevent possible intrusions. In particular, S-RAF instructs
the SDN Controller (SDN-C) to redirect the potential cyberattackers to the EPES/SG
honeypots. The EPES/SG honeypots constitute a security control of S-RAF. Next, XL-EPDS
uses statistics originating from the SDN-C to detect possible anomalies or cyberattacks
related to the entire SDN network. Finally, SDN-SELF communicates with the SDN-C in
order to mitigate possible intrusions and anomalies. The following subsections analyse the
components and the interfaces of each SDN-microSENSE framework.
A more detailed view of the SDN-microSENSE architecture, along with the interfaces
between the various planes, is depicted in Figure 1. The structural view is based on the
SDN architecture, as deﬁned by the Open Networking Foundation (ONF) [
] and Request
for Comments (RFC) 7426 [
], and follows the rationale of decoupling the network control
with the forwarding functions. Therefore, according to the above speciﬁcations, the con-
ceptual frameworks are placed within the Data, Controller, Application, and Management
Planes. In particular, the Data Plane contains the EPES/SG infrastructure, the honeypots
and the SDN switches. The Controller Plane consists of multiple SDN controllers that
receive guidance from the Application and Management Planes and conﬁgure the Data
Plane accordingly. The conceptual frameworks and their components are placed within the
Application Plane. In this plane, the most important operational decisions take place, such
as the detection of a cyberattack or the decision to isolate a malicious network ﬂow. Finally,
the Management Plane provides all complementary functionalities related to the system
usability, including dashboard, databases, and privacy preserving mechanisms to ensure
the privacy of data subjects affected by the SDN-microSENSE operation. It is worth men-
tioning that the Management Plane is placed vertically since it provides complementary
services to all planes. Indicatively, the Asset Inventory database is used by all components
of the Application Plane in order to access information related to the underlying EPES/SG
components. Concurrently, the SDN Synchronisation and Coordination Service (SCS) is ac-
cessed by both the Application Plane and the Controller Plane to retrieve the master SDN-C
for a particular switch and carry out the master SDN-C election process, respectively.
detection, ML/DL-based detection,
Security Events correlation, visual-
based detection, Anonymous
Repository of Incidents
SDN-microSENSE Business Logic
Integrated platform which harmonizes Security Management & Risk
Assessment, Intrusion Detection & Correlation, Privacy-Preserving, Self
Healing and Energy Management under the umbrella of SDN. Multiple
technologies: SDN, Honeypots, SIEM, IDPS, Machine Learning, MISP,
visual analytics and blockchain.
Intrusion Detection & Correlation,
Security Management &
Data Plane &
Figure 2. SDN-microSENSE Business Logic.
3.1. S-RAF: SDN-microSENSE Risk Assessment Framework
S-RAF is a framework that undertakes to implement collaborative and dynamic
risk management. Moreover, apart from this role, S-RAF includes a set of EPES/SG
honeypots that hide and protect the real EPES/SG assets. The following subsections
analyse both the collaborative risk assessment and the EPES/SG honeypots of the SDN-
3.1.1. Security Management and Risk Assessment
An essential function of the SDN-microSENSE architecture is the collaborative and
dynamic risk assessment. To this end, S-RAF follows a methodology consisting of seven
steps: (a) determining the goal of the EPES risk assessment, (b) analysis of the EPES organi-
sations, (c) EPES cyberthreat analysis, (d) vulnerability analysis, (e) impact analysis
assessment and (g) risk mitigation. Thus, following this methodology, S-RAF receives the
security events and alerts coming from XL-EPDS and incorporates into this information a
cumulative risk value for each involved asset and the corresponding connections.
3.1.2. EPES/SG Honeypots and Honeypot Manager
According to [
], a honeypot is "an information system whose value lies in unautho-
rised or illicit use of the resource". In other words, honeypots are commonly used as an
extra security layer in order to act as a decoy, which lures the cyberattackers and captures
useful information about their identity and activities [
]. SDN-microSENSE provides a
variety of EPES/SG honeypots that implement realistic emulations for three EPES/SG com-
munication protocols: (a) IEC-61850, (b) IEC-60870-5-104, and (c) Modbus/TCP. In more
detail, the IEC-61850 honeypot emulates real intelligent electronic devices usually located
in circuit breakers of the substations by parsing the Intelligent Capability Description (ICD)
ﬁles. On the other side, the IEC-60870-5-104 and Modbus/TCP honeypots rely on Conpot.
Furthermore, it is noteworthy that the Modbus/TCP honeypot can imitate the responses of
the real EPES/SG assets by integrating a generative adversarial network.
The deployment and the lifecycle management of the aforementioned EPES/SG
honeypots are provided by the Honeypot Manager (HM). The HM constitutes a web-based
interface, which allows the security administrator to inspect the security events and alerts
received by XL-EPDS and decides regarding the deployment of an EPES/SG honeypot.
In addition, the HM leverages the northbound interface of the SDN-C by dynamically
redirecting the malicious network trafﬁc towards the EPES/SG honeypots. The redirection
can be activated manually by the HM operator based on the security events and alerts
received by XL-EPDS. This mechanism aims to enforce the cyberattackers to react with the
EPES/SG honeypots, thus collecting useful information about their activities.
3.2. XL-EPDS: Cross Layer Energy Prevention and Detection System
The XL-EPDS framework utilises various kinds of data in order to detect timely
and reliably potential EPES/SG intrusions and anomalies. To this end, the framework
integrates a SIEM system especially designed for the energy sector. The proposed SIEM
system called XL-SIEM includes a plethora of intrusion and anomaly detectors related to
the EPES/SG communication protocols. Moreover, it ensures the privacy of the involved
entities through the Overlay Privacy Framework (OPF). Finally, XL-EPDS incorporates an
anonymous repository of incidents called ARIEC, which allows the EPES organisations
to share with each other the cybersecurity incidents. Each XL-EPDS component is further
3.2.1. XL-SIEM and Detectors
XL-SIEM composes a SIEM system capable of detecting multiple EPES cyberattacks by
allowing the interconnection with a myriad of security detectors. In particular, the XL-SIEM
consists of (a) XL-SIEM agents for processing information received from security detectors
and distributed across the EPES infrastructure and (b) the XL-SIEM core, which integrates
an event correlation engine, a database and a management dashboard. The security detec-
tors are deployed throughout the EPES infrastructure and undertake to recognise various
EPES cyberattacks and anomalies, generating the respective security logs. To this end,
both signature/speciﬁcation-based techniques and ML/DL-based methods are applied.
is used with the
signatures and speciﬁcation rules devel-
oped during the project. Next, a set of ML/DL-based detectors [
] are responsible for
discriminating cyberattacks and anomalies against a plethora of EPES protocols, such as
Modbus/TCP, DNP3, IEC 61850 (Generic Object Oriented Substation Event (GOOSE)), IEC
60870-5-104, Message Queuing Telemetry Transport (MQTT) and Network Time Protocol
(NTP). Moreover, there is a detector called
, which is able to discriminate poten-
tial anomalies related to the entire SDN network based on the statistics given by the SDN-C.
OPF constitutes another detector of XL-SIEM, ensuring the privacy of EPES/SG entities
and transferring relevant security logs to XL-SIEM whether they are relevant violations.
Finally, the Discovery tool constitutes a visual-based anomaly detector, which provides the
appropriate visual interfaces through which the security administrator can distinguish the
presence of an anomaly that possibly cannot be detected by the aforementioned detectors.
Next, the XL-SIEM agents are responsible for collecting and normalising the various secu-
rity logs generated by the XL-SIEM detectors with a standardised format. The normalised
events are called security events and are transmitted by the XL-SIEM agents to the XL-SIEM
engine or external components. Subsequently, the XL-SIEM engine receives the security
events and correlates them, thus producing security alerts. A security alert is deﬁned as a
set of security events related to each other through the correlation rules deﬁned by security
experts. Finally, the XL-SIEM database and XL-SIEM dashboard store and visualise the
security events and alerts generated by XL-SIEM, respectively.
3.2.2. ARIEC: Cloud-Based Anonymous Repository of Incidents
To be aligned with the Directive on security of Network and Information Systems
], which requires mandatory reporting of the cybersecurity incidents by the
EPES organisations, the SDN-microSENSE architecture introduces ARIEC, which is a
repository of anonymised security events and alerts originating from XL-SIEM. In the
context of ARIEC, both security events and alerts are called cybersecurity incidents. They
are also accompanied by the risk information calculated by S-RAF. Therefore, ARIEC
allows storing and sharing technical details of the cybersecurity incidents among different
EPES organisations belonging to a trusted network without identifying the victim identity
or other sensitive information that can affect the reputation of the EPES organisation.
ARIEC follows a centralised architecture, which relies on the Malware Information Sharing
Platform (MISP) and anonymisation procedures based on the differential privacy and
Natural Language Processing (NLP) techniques.
3.3. SDN-SELF: SDN-enabled Self-hEaLing Framework
The goal of the SDN-SELF framework is twofold. First, it mitigates the possible
anomalies and intrusions detected by XL-EPDS. Secondly, SDN-SELF is responsible for the
energy management and optimisation required after the mitigation processes. In particular,
SDN-SELF comprises ﬁve components: (a) Electric Data Analysis Engine (EDAE),
Islanding and optImisation fraMework (IIM), (c) the rEstoration Machine-learning frame-
wOrk (EMO) and (d) the Blockchain-based Energy Trading System. Each component of the
SDN-SELF framework is further analysed in the following subsections, respectively.
3.3.1. EDAE: Electric Data Analysis Engine
Leveraging the SDN programming capabilities, EDAE undertakes to maximise the
grid observability and protect the EPES/SG infrastructure in case of cyberattacks or failures.
In particular, EDAE continuously monitors the underlying network against Quality of
Service (QoS) constraints (e.g., latency and available bandwidth) provided by the EPES/SG
operator and the cybersecurity incidents delivered by S-RAF. In comparison to existing state
of the art, refs. [
] EDAE aims to combine the satisfaction of QoS, security and observ-
ability requirements in a single optimisation scheme. Three main scenarios are distinguished,
Scenario A: QoS constraints are not satisﬁed. Supposing the communication quality is
degraded in a manner that criteria of minimum latency cannot be satisﬁed. In that
case, EDAE employs the PaDe [
] genetic algorithm in order to decompose the multi-
objective problem of path reconstruction to multiple single-objective ones that are
resolved using the asynchronous generalised island model to distribute the solution
]. The ﬁnal solution (i.e., the optimal path that maximises the grid QoS
and observability) is obtained as the set of the best individual solution in each single-
Scenario B: An EPES/SG device is disconnected from the network. In a more speciﬁc scenario
that a Phase Data Concentrator (PDC) is disconnected from the network, the Phasor
Measurement Units (PMUs) connected to that PDC should be reallocated to the next
available PDC so that the security and QoS constraints are not validated for any of the
existing PMUs. In this case, a Mixed-Integer Linear Programming algorithm chooses
and applies the best PMU reallocation scheme to minimise the overall network latency.
This problem is also studied by [
]; however, authors are limited to maximising
observability, while EDAE also addresses QoS and security requirements.
Scenario C: Change of security risk. Supposing that the security risk of an intermediate
switch changes dramatically, EDAE ﬁnds alternative paths so that the risk level of
the rest infrastructure will be maintained while the QoS requirements of the rest
applications are intact.
3.3.2. IIM: Islanding and optImisation fraMework
The purpose of IIM is to preserve the stability of the EPES infrastructure by offering
intentional islanding schemes in case of severe disturbances (e.g., disruptions caused by cy-
berattacks, extreme natural phenomena or human errors), thus avoiding cascading failures
that can potentially lead to a blackout. Activated as a response to speciﬁc security incidents
received from S-RAF, IIM collects information regarding the triggering event, as well as the
current status of the grid, and delivers appropriate islanding recommendations, which are
evaluated and applied by the system operator. More speciﬁcally, the islanding solutions
aim to partition the grid into several segments, creating islands that isolate the affected
assets and at the same time minimise the power imbalance while maintaining supply to the
maximum number of consumers. IIM employs two different methods for calculating the
islanding schemes, namely: (1) a genetic algorithm, which provides the optimal solution
at the cost of increased time-complexity and (2) a deep learning architecture [
addresses the islanding problem by utilising graph convolutional neural networks, able to
provide the solution in real-time.
3.3.3. EMO: rEstoration Machine-learning framewOrk
EMO acts as a modern energy restoration and management framework, incorporating
procedures for restoring the electrical grid when there are failures, thus avoiding further
damage to the EPES/SG infrastructure. Towards this goal, EMO continuously observes the
grid status, aiming to identify islanding cases and automatically commences the required
restoration and management processes, ensuring the real-time operation through the
optimal allocation of the network capacity. In more detail, the key functionalities of this
component are the following:
Regulating the local variables of Distributed Energy Resources (DERs) (i.e., voltage
and frequency), to achieve high power quality that leads to less losses and results in
more robust islands in terms of load-balancing capabilities.
Maintaining the stability of the electrical grid and balancing the available energy of
Managing load shedding, including decisions on when, where, and how much load
should be shed according to the priorities at each island, in order to mitigate the
impact to the end-users.
Computing the energy exchange feasibility within the islands, after receiving the
trading requests from the Blockchain-based Energy Trading System.
At its core, EMO consists of two modules, the ﬁrst responsible for the economic
management of the power ﬂow between the DERs and the second undertaking to control
the voltage-reactive and the frequency-active power, based on a hybrid multi-agent system
that optimally allocates the requested energy between the units.
3.3.4. Blockchain-Based Energy Trading System
The Blockchain-based Energy Trading System is placed on top of SDN-SELF and aims
to secure transactions taking place among the islanded parts of the EPES/SG. In more
detail, it consists of two modules, namely the e-auction module and the Blockchain-based
Intrusion and Anomaly Detection (BIAD) module. The e-auction module establishes secure
and trustworthy networks among the parties involved in energy transactions, including
consumers and prosumers and Energy Service Company Organisations that manage the
ﬁnancial transactions. The Vickrey-Clarke-Groves (VCG) [
] mechanism is adopted by
e-auction with the aim to reveal the actual valuations of the user’s bids by concealing the
bids submitted by other users. The communication among the participants is performed
through a fabric blockchain network based on the Hyperledger Fabric. Finally, the status
of each participating device (e.g., smart meters) is monitored by BIAD. In particular,
BIAD constitutes an XL-SIEM detector, which monitors the integrity of the various logs,
transmitting the corresponding security logs to XL-SIEM.
3.4. SDN Controller
The SDN-C undertakes to program the underlying intermediary network devices (i.e.,
SDN switches) according to the instructions from the Application Plane, using OpenFlow
v1.3. Based on the
SDN controller [
], the SDN-C is a multi-modular application
that deploys multiple modules that extend the
functionalities. In particular, SDN-C
integrates the following new modules: (a)
, and (b) the
module. In more detail, the
module undertakes to re-actively ﬁll
the OpenFlow tables of the underlying SDN switches. In comparison to the original
implementation, the enhanced reactive application of SDN-microSENSE keeps a record
of source MAC addresses and ingress ports of Ethernet frames; therefore, the SDN-C can
detect cases of broadcast storms and inserts the corresponding OpenFlow rules to prevent
them. The loop-free topology relies on EDAE in order to apply optimisations and enable
redundant paths. The SDN-C undertakes to program the underlying intermediary network
devices (i.e., SDN switches) according to the instructions from the Application Plane, using
OpenFlow v1.3. Based on the
SDN controller [
], the SDN-C is a multi-modular
application that deploys multiple modules that extend the
functionalities. In particular,
SDN-C integrates the following new modules: (a)
, and (b) the
module. In more detail, the
module undertakes to
re-actively ﬁll the OpenFlow tables of the underlying SDN switches. In comparison to
implementation, the enhanced reactive application of SDN-microSENSE
keeps a record of source MAC addresses and the ingress ports of Ethernet frames; therefore,
the SDN-C can detect cases of broadcast storms and inserts the corresponding Open-
Flow rules to prevent them. The loop-free topology relies on EDAE in order to apply
optimisations and enable redundant paths.
4. SDN-microSENSE Use Cases and Implementation Considerations
SDN-microSENSE intends to address security and privacy requirements that cover the
whole energy value chain, involving traditional electricity generators, Transmission System
Operators (TSOs), Distribution System Operators (DSOs), DER operators and prosumers.
The full potential of the proposed architecture is demonstrated and validated through six
use cases/pilots that address various cybersecurity requirements in the area of EPES/SG:
Use Case 1 - Investigation of Versatile Cyberattack Scenarios and Methodologies Against
EPES: This use case deals with a variety of cybersecurity threats against substations,
including station and process buses.
Use Case 2 - Massive False Data Injection Cyberattack Against State Operation and Auto-
matic Generation Control: This use case focuses on false-data injection attacks against
the whole energy value chain, including generation (power plants), TSO and DSO
substation architectures as well as smart metering infrastructures.
Use Case 3 - Large-scale Islanding Scenario Using Real-life Infrastructure: The third use case
treats the aftermath of a cyberattack or critical failure that results in an unbalanced
grid. The SDN-microSENSE platform acts as a decision support system for the TSO
in order to decide on intentionally islanding segments of the affected grid or to shed
redundant load in order to balance energy demand and supply .
Use Case 4 - EPES Cyber-defence against Coordinated Attacks: This use case aims to
evaluate the SDN-microSENSE platform against the detection and mitigation of
coordinated cyberattacks, taking place in substations.
Use Case 5 - Distribution Grid Restoration in Real-world PV Microgrids: This use case
deals with the detection and mitigation of cyberthreats occurring in the industrial
network of a real photovoltaic station.
Use Case 6 - Realising Private and Efﬁcient Energy Trading among PV Prosumers: This use
case realises the decentralized energy trading environment that SDN-microSENSE
proposes, by involving PV prosumers.
Unarguably, the SDN technology is one of the main enablers that pave the way to
a holistic cybersecurity solution that addresses detection and mitigation of cyberthreats.
However, it should be noted that SDN introduces new organisational and technical chal-
lenges for potential end-users.
First of all, the required technologies (e.g., OpenFlow) require replacement or upgrade
of the intermediary network equipment. On top of that, compatibility and vendor inte-
gration issues may arise due to vendor-speciﬁc implementations that deviate from the
standards. Moreover, the IT personnel needs to have specialized knowledge on SDN in
order to troubleshoot network issues caused by the SDN control. To sum up, despite its
beneﬁts on network management, SDN may introduce unforeseen technical and manage-
rial complications, increase ﬁnancial costs during adoption, and possibly be rejected by the
management if the drawbacks outweigh the beneﬁts .
Understanding the concerns of EPES/SG operators on ensuring business continuity,
SDN-microSENSE intends to alleviate the drawbacks of SDN by providing unique op-
timisation and network security options (e.g., detection and isolation of cyberthreats at
the access layer [
]), which would be unavailable without the SDN technology. More-
over, business continuity is ensured since the coordination of multiple SDN-Cs employed
by SDN-microSENSE prevents the single point of failure caused by software failures or
cyberattacks against the Controller Plane.
The rise of the IIoT transforms the typical EPES model into a new digital era, thus
introducing multiple beneﬁts. However, this progression creates severe cybersecurity and
privacy issues. This paper presents the SDN-microSENSE architecture, which introduces a
set of cybersecurity and privacy mechanisms based on the umbrella of the SDN technology.
SDN-microSENSE deﬁnes three main frameworks: S-RAF, XL-EPDS and SDN-SELF. S-RAF
applies a collaborative and dynamic risk assessment, thus determining the risk related to
each security event and alert. The security events and alerts are generated by XL-EPDS via
advanced intrusion detection and correlation mechanisms. Finally. SDN-SELF introduces a
set of mitigation and energy management actions that can ensure the normal operation of
the EPES/SG organisations.
Conceptualization, P.R.G., P.S., C.D., T.L.; Methodology. I.L.P., R.T.B., R.D.;
Software, P.R.G., P.S., C.D., Y.S., G.E., T.L., A.S. (Achilleas Sesis), A.S. (Antonios Sarigiannidis),
R.T.B., D.P., S.A.M., G.L., A.P., T.K., A.D., O.M., A.C.S., P.P.S., J.L.D.-G., M.E., M.M.A., B.C., F.R.;
Investigation, P.R.G., P.S., C.D., Y.S., G.E., T.L., A.S. (Achilleas Sesis), A.S. (Antonios Sarigiannidis),
R.T.B., D.P., S.A.M., G.L., A.P., T.K., A.D., O.M., A.C.S., P.P.S., J.L.D.-G., M.E., M.M.A., B.C., F.R.;
Validation, V.G., S.K., H.C.B., D.-E.A., R.G.; Resources, N.P.; Writing—Original Draft Preparation,
P.R.G., C.D., Y.S.; Writing—Review & Editing, P.R.G., C.D., Y.S., T.R.; Supervision, P.S., T.L.; Project
Administration, A.A. All authors have read and agreed to the published version of the manuscript.
This project has received funding from the European Union’s Horizon 2020 research and
innovation programme under grant agreement No. 833955.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
This project has received funding from the European Union’s Horizon 2020
research and innovation programme under grant agreement No. 833955.
Conﬂicts of Interest:
The authors declare no conﬂict of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or
in the decision to publish the results.
The following abbreviations are used in this manuscript:
ABOD Angle-Based Outlier Detection
ADT Attack Defence Tree
APT Advanced Persistent Threat
BIAD Blockchain-based Intrusion and Anomaly Detection
CVE Common Vulnerabilities and Exposures
CVSS Common Vulnerability Scoring System
DAS Distribution Automation System
DDoS Distributed Denial-of-Service
DERs Distributed Energy Resources
DNP3 Distributed Network Protocol 3
DSO Distribution System Operator
EDAE Electric Data Analysis Engine
EMO rEstoration Machine-learning framewOrk
EPES Electrical Power and Energy Systems
GOOSE Generic Object Oriented Substation Event
HM Honeypot Manager
ICD Intelligent Capability Description
ICS Industrial Control System
IDPS Intrusion Detection and Prevention System
IDS Intrusion Detection System
IIM Islanding and optImisation fraMework
IIoT Industrial Internet of Things
IoT Internet of Things
IP Internet Protocol
IT Information Technology
LOF Local Outlier Factor
MAC Medium Access Control
MISP Malware Information Sharing Platform
ML Machine Learning
MQTT Message Queuing Telemetry Transport
NIS Network and Information System
NLP Natural Language Processing
NTP Network Time Protocol
ONF Open Networking Foundation
OPF Overlay Privacy Framework
OVS Open vSwitch
PCA Principal Component Analysis
PDC Phase Data Concentrator
PMU Phasor Measurement Unit
QoS Quality of Service
RFC Request for Comments
SCADA Supervisory Control and Data Acquisition
SCS Synchronisation and Coordination Service
SDN Software-Deﬁned Networking
SDN-C SDN Controller
SDN-SELF SDN-enabled Self-healing Framework
SG Smart Grid
SIEM Security Information and Event Management
SOS Stochastic Outlier Selection
S-RAF SDN-microSENSE Risk Assessment Framework
SVM Support Vector Machine
TCP Transmission Control Protocol
TSO Transmission System Operator
XL-EPDS Cross-Layer Energy Prevention and Detection System
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