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sensors
Article
Security Information and Event Management (SIEM):
Analysis, Trends, and Usage in Critical Infrastructures
Gustavo González-Granadillo * , Susana González-Zarzosa and Rodrigo Diaz
Citation: González-Granadillo, G.;
González-Zarzosa, S.; Diaz, R.
Security Information and Event
Management (SIEM): Analysis,
Trends, and Usage in Critical
Infrastructures. Sensors 2021,21, 4759.
https://doi.org/10.3390/s21144759
Academic Editor: Alexios Mylonas
and Nikolaos Pitropakis
Received: 3 June 2021
Accepted: 8 July 2021
Published: 12 July 2021
Publisher’s Note: MDPI stays neutral
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Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
ATOS Spain, Atos Research & Innovation, Cybersecurity Unit, 28037 Madrid, Spain;
susana.gzarzosa@atos.net (S.G.-Z.); rodrigo.diaz@atos.net (R.D.)
*Correspondence: gustavo.gonzalez@atos.net
Abstract:
Security Information and Event Management (SIEM) systems have been widely deployed
as a powerful tool to prevent, detect, and react against cyber-attacks. SIEM solutions have evolved
to become comprehensive systems that provide a wide visibility to identify areas of high risks and
proactively focus on mitigation strategies aiming at reducing costs and time for incident response.
Currently, SIEM systems and related solutions are slowly converging with big data analytics tools. We
survey the most widely used SIEMs regarding their critical functionality and provide an analysis of
external factors affecting the SIEM landscape in mid and long-term. A list of potential enhancements
for the next generation of SIEMs is provided as part of the review of existing solutions as well as an
analysis on their benefits and usage in critical infrastructures.
Keywords: evolution of SIEMs; SIEM enhancement; SIEM trends; critical infrastructures
1. Introduction
Cybersecurity risks affecting industrial control systems (ICT) have grown enormously
during the past couple of years, mainly due to increased activity by nation-states and cyber
criminals. Attackers have become more sophisticated and dangerous and their appropriate
and timely detection has become a real challenge. Examples of current cybersecurity
incidents affecting IT and ICT are [
1
]: ransomware attacks; malware having impact on
the utility’s ability to conduct business and operations; phishing campaigns directed to
executives, executive assistants, SCADA engineers, IT administrators or other privileged
users; business email compromise incidents, including account takeover or impersonation
of executives; data leakage and thefts; social engineering to gather sensitive information
from personnel.
According to a recent report from NIST [
2
], cybersecurity solutions in industrial con-
trol systems should provide real-time behavioral anomaly detection, enable faster incident
management and allow for intelligent visualization of the network and all its intercon-
nected nodes. Security Information and Event Management (SIEM) systems consider the
aforementioned capabilities as built-in features.
In general, SIEMs have the capacity to collect, aggregate, store, and correlate events
generated by a managed infrastructure [
3
]. They constitute the central platform of modern
security operations centers as they gather events from multiple sensors (intrusion detection
systems, anti-virus, firewalls, etc.), correlate these events, and deliver synthetic views of
the alerts for threat handling and security reporting [
4
,
5
]. Besides these key capacities,
there are many differences between the existing systems that normally reflect the different
positions of SIEMs in the market.
Several companies have developed SIEM software products in order to detect network
attacks and anomalies in an IT system infrastructure. Among them, we can find classical IT
companies (e.g., HP, IBM, Intel, McAfee), others with more visionary options (e.g., AT&T
Cybersecurity/AlienVault’s SIEMs), and promising tools to be taken into consideration in
a SIEM context (e.g., Splunk).
Sensors 2021,21, 4759. https://doi.org/10.3390/s21144759 https://www.mdpi.com/journal/sensors
Sensors 2021,21, 4759 2 of 28
In this paper, we review the most widely used security information and event manage-
ment tools (commercial and open source) aiming at identifying their main characteristics,
benefits, and limitations to detect and react against current attack scenarios. We provide
an in-depth analysis of the features and capabilities of current SIEMs and focus on their
limitations in order to propose potential enhancements to be integrated into current SIEM
platforms. An analysis of external factors (e.g., political, economical, societal) that could
potentially affect future SIEMs in the mid and long term is provided as a way to identify
enablers and barriers to the new generation of SIEM systems. In addition, an overview of
SIEM solutions in critical infrastructures is provided to identify potential usage of these
tools. To the best of our knowledge, this paper is the first academic work to systematically
analyze the current landscape of SIEM systems.
Paper Organization: The remainder of the paper is structured as follows: Section 2
introduces the main commercial and open source SIEM solutions available on the market.
Section 4analyzes the limitations of current SIEMs and presents potential capabilities for
enhancements. Section 5analyzes the future of SIEMs based on external factors. Section 6
proposes potential enhancements for the next generation of SIEMs. Section 7provides an
overview of the importance and usage of SIEM systems in critical infrastructures. Related
works are presented in Section 8. Finally, conclusions are presented in Section 9.
2. SIEM Solutions
Security Information and Event Management (SIEM) systems have been developed
in response to help administrators to design security policies and manage events from
different sources. Generally, a simple SIEM is composed of separate blocks (e.g., source
device, log collection, parsing normalization, rule engine, log storage, event monitoring)
that can work independently from each other, but without them all working together, the
SIEM will not function properly [
3
]. Figure 1depicts the basic components of a regular
SIEM solution.
Figure 1. SIEM basic components.
SIEM platforms provide real time analysis of security events generated by network
devices and applications. In addition, even though the new generation of SIEMs provide
response abilities to automate the process of selecting and deploying countermeasures,
current response systems select and deploy security measures without performing a com-
prehensive impact analysis of attacks and response scenarios.
Besides these common features, current SIEMs present differences that classify them
as leaders, challengers, niche players, or visionaries, according to the Gartner’s SIEM Magic
Quadrant annual report. This section introduces the main SIEM solutions available on the
market to date and provides the main advantages and drawbacks of each of them based on
the most recent Gartner report and research works related to the SIEM technologies [
6
–
19
].
Please note that in this section we have considered the list of SIEM solutions proposed
by Gartner during the last decade in their annual Magic Quadrant report, as such, the list
of SIEM vendors presented in Table 1makes reference only to the solutions selected and
published by Gartner, and discards other commercial and open-source SIEMs that did not
meet Gartner’s criteria.
Sensors 2021,21, 4759 3 of 28
2.1. SIEM Classification
The analysis and evaluation of security systems have been widely proposed in the
literature. While some research focuses on the commercial aspects, others concentrate
on the technical features that could be improved in current SIEM solutions. Well known
institutions like Gartner [
20
], for instance, propose a commercial analysis of SIEM systems
based on the market and major vendors, for which a report is released on an annual basis
to position SIEM vendors as market leaders, challengers, niche players, or visionaries.
Other security institutions (e.g., Techtarget (http://searchsecurity.techtarget.com/
accessed on 12 January 2021) and Info-Tech Research Group (http://www.infotech.com/
accessed on 12 January 2021)), have widely reported on the capabilities of SIEM solutions
and on the way SIEM vendors can be compared and assessed. Techtarget, on the one hand,
releases periodic electronic guides about securing SIEM systems and how to define SIEM
strategy, management and success in the enterprise [
21
]. Info-Tech, on the other hand,
provides technical reports on the SIEM vendor landscape [
?
] focusing on the benefits
and drawbacks of major commercial SIEMs. Both organizations take the Gartner Magic
Quadrant as the baseline for their analysis.
During the last decade, Gartner has classified SIEM solutions as leaders (organizations
that execute well against their current vision and are well positioned for tomorrow),
visionaries (organizations that understand where the market is going or have a vision for
changing market rules, but do not yet execute well), niche players (organizations that focus
successfully on a small segment or are unfocused and do not out-innovate or outperform
others), and challengers (organizations that execute well today or may dominate a large
segment, but do not demonstrate an understanding of market direction).
Table 1shows the evolution of SIEM solutions (and SIEM vendors) from 2010 to
2020. Note that the latest report to date was released in January 2020 (no report was
delivered in 2019). From Table 1, the star indicates those that have been leading the
market, challengers are identified with a lozenge, niche players are identified with a
triangle, and visionaries are identified with a square. It is important to highlight that
very few of them appeared every year in the top ranking assessment during the whole
decade period. This is the case of RSA, the security division of EMC Corporation (Dell
Technologies), which offers a NetWitness Platform evolved SIEM; IBM, which offers a
tool called Qradar; NetIQ/Microfocus/ArcSight, offering the ArcSight Enterprise Security
Manager; McAfee/Intel, offering the McAfee Enterprise Security Manager, and LogRhythm,
offering the Nextgen SIEM platform.
Note that some solutions have been merged to keep with the changes and evolution of
the market. This is the case of IBM and Q1 labs (that offered a joined solution in 2012 and
2013); NetIQ and Novell (2012); HP and ArcSight (2013); AccelOps and Fortinet (2016), and
more recently, Micro Focus and ArcSight, as well as Micro Focus and NetIQ (2017). Some
of these joined solutions are no longer available in 2021.Another important aspect to note
is that some SIEM vendors have been in the top Gartner classification list ever since they
first appeared in the market (e.g., Splunk, AlientVault/AT&T Cybersecurity, SolarWinds,
EventTracker, Fortinet, MicroFocus). Some others have joined the list in the past couple
of years with User and Entity Behavior Analytics (UEBA) features (i.e., Manage Engine,
Venustech, Rapid7, Exabeam, Secureonix, LogPoint, and HanSight).
A recent study [
22
] considers 22 players in the 2020 SIEM vendor map based on three
main capabilities: (i) threat intelligence detection, (ii) compliance, and (iii) log manage-
ment. Besides threat intelligence, compliance, and log management, SIEM developers are
considering UEBA capabilities and smart dashboards as innovations to be added to their
solutions. As a result, new SIEM systems will help security administrators with pre-built
dashboards, reports, incident response workflows, advanced analytics, correlation searches,
and security indicators [
23
]. In addition, an in-depth analysis of SIEMs extensibility [
19
]
revealed that current SIEM solutions need to improve features such as behavioral analysis,
risk analysis and deployment, visualization, data storage, and reaction capabilities, in order
to keep up with the market.
Sensors 2021,21, 4759 4 of 28
Table 1. SIEM vendors classification.
SIEM Vendor
2010
2011
2012
2013
2014
2015
2016
2017
2018
2020
HP/ArcSight/HPE [24]F F F F F F F
RSA/EMC [25]F F F F
SenSage [26]F N N
LogLogic [27]F F
Symantec [28]F F
Q1Labs [29]F F F F
Novell [30]F F F
IBM [31] F F F F F F F F
Quest Software [32]
CA [33]
Tenable [34]N N N N
Prism Microsystems [35]NN
LogMatrix [36]N
NetIQ/Microfocus [37] N F N N N N
McAfee/Intel [38] F F F F F F F F N
Trustwave [39] N N N N N
LogRhythm [40] F F F F F F F F
TriGeo [41]
netForensics [42]
eIQnetworks [43] N
Splunk [44]N F F F F F F F
Tripwire [45]N
AlienVault/ AT&T
Cybersecurity [46]
N N N N
Correlog [47]N N
S21sec [48]N N
Tango/04 [49]N N
Tier-3 [50]
SolarWinds [51] N N N N N N
Tibco-LogLogic [52]
EventTracker [53]N N N N N N
AccelOps/Fortinet [54]N N N N N N
Blackstratus [55]N N N N N
Manage Engine [56]N N N N
FireEye [57]N N
Venustech [58]N N
Rapid7 [59] F
Exabeam [60] F F
Securonix [61] F F
LogPoint [62]N
HanSight [63]N
FLeader Challenger NNiche Player Visionary.
2.2. SIEM Tools
Considering the previous information about SIEMs, Table 2summarizes some of the
most promising SIEMs to date.
Sensors 2021,21, 4759 5 of 28
Table 2. SIEM tools/vendor characteristics.
ArcSight Enterprise Security Manager
(MicroFocus/ HPE/ NetIQ)
Provides a graphical interface for the
Security Operations Center (SOC) team
and a set of applications or external
commands that help the correlation
and/or investigation processes.
Limited visualization options and
intricate correlation rules [17]. The
information associated with events is
immutable, with evident deficits when it
comes to adapting the product to
company processes and needs.
Qradar (IBM)
Can be deployed as a hardware, software,
or virtual appliance, as well as a Software
as a Service (SaaS) on the IBM cloud.
Provides a user interface for real-time
event and view, reports, offenses, asset
information, and product management.
Offers support for threat
intelligence feeds.
Provides basic reaction capabilities that
include reporting and alerting functions.
The endpoint monitoring for threat
detection and response, or basic file
integrity requires the use of third-party
technologies.
McAfee Enterprise Security Manager
(McAfee/ Intel)
Allows for scalable and versatile SIEM
architecture, delivering real-time
forensics, comprehensive application and
database traffic/content monitoring,
advanced rule and risk-based correlation
for real-time as well as historical incident
detection and automatic reaction.
Requires the use of additional solutions
(e.g., McAfee Active Response).
Predictive analytics and other built-in
features such as behavioral analysis are
poorly developed.
LogRhythm Next GEN SIEM Platform
(LogRhythm)
Provides end-point monitoring, network
forensics, user and entity behavior
analytics, and response capabilities. Can
be deployed in an appliance, software or
virtual instance supporting scalable
decentralized architectures
Unsuitable for organizations with critical
infrastructures although extensions can
be deployed to enhance the SIEM
capabilities. Requires high degree
automation and out-of-the-box content.
USM and OSSIM (AT&T Cybersecurity/
AlienVault)
Offers both commercial solutions (i.e.,
Alienvault Unified Security
Management-USM ) and open source
SIEM solutions. (i.e., OSSIM). Includes a
web-based graphical interface for
administration, reporting and security
event management.
Limited user or entity behavior analytics
as well as machine learning capabilities.
Basic reaction capabilities (e.g., send
email, execute script, open ticket) and
limited to the pre-defined set of
conditions associated to a security policy.
RSA Netwitness Platform (Dell)
Analyzes data and behavior of people
and processes within a network across a
company’s logs, packets, and end-points.
Focuses on advanced threat detection.
Provides strong OT
monitoring capabilities
It requires a wide understanding of the
breadth of the options and the
implications for cost, functionality,
and scalability.
Splunk Enterprise Security (Splunk)
Market-leading platform in Operational
Intelligence. Offers data collection,
indexing, and visualization capabilities
for security events monitoring. Uses
advanced security analytics, which
include both unsupervised machine
learning and user behavior capabilities.
Uses basic predefined correlation rules
for monitoring and reporting
requirements. Reaction capabilities are
limited to email notifications. Requires
integration with third-party applications
for task and workflow automation.
SolarWinds Log and event Manager
(SolarWinds)
Provides centralized log collection and
normalization, automated threat
detection and response, intuitive
visualization, and user interface, as well
as real time correlation and log searching
to support investigation.
Lacks support for monitoring public
cloud services’ IaaS or SaaS. Does not
support custom report writing and
customization of out-of-the-box
compliance report templates.
Sensors 2021,21, 4759 6 of 28
3. SIEM Features and Capabilities
Fundamentally, all SIEMs have the capacity to collect, store, and correlate events
generated by a managed infrastructure [
64
]. Besides these key capacities, there are many
differences between existing systems that normally reflect the different positions of SIEMs
in the market. This section provides a list of features to be considered in the analysis of
SIEM solutions. Based on our experience with different commercial SIEMs and contrasting
the identified information related to the usage of commercial and open-source SIEMs
from the literature, Table 3summarizes this analysis and assesses each SIEM feature as
low/basic (poorly implemented or not implemented at all), average (partly implemented),
or high/advanced (fully implemented) for the most promising SIEM solutions described
in Table 2. Please note that this assessment only includes the basic configuration of the
selected SIEM solutions, no additional features (add-ons) are considered in the analysis.
Table 3. Analysis of different SIEM solutions.
Functionality
ArcSight
QRadar
McAfee
LogRhythm
USM-OSSIM
RSA
Splunk
SolarWinds
Correlation rules ◦ ◦ • • • ◦ − •
Data sources • • • ◦ ◦ • • ◦
Real time processing • • • • • • • •
Data volume • ◦ • ◦ ◦ ◦ • ◦
Visualization − ◦ ◦ ◦ ◦ ◦ • ◦
Data analytics ◦ • ◦ • ◦ ◦ • ◦
Performance ◦ ◦ • ◦ ◦ • ◦ •
Forensics − • • ◦ • • ◦ ◦
Complexity • ◦ ◦ ◦ ◦ • • •
Scalability • • • • − • • •
Risk analysis − ◦ ◦ ◦ − ◦ − ◦
Storage ◦ ◦ • ◦ ◦ ◦ ◦ •
Price • • • ◦ ◦ • • ◦
Resilience ◦ • • ◦ ◦ • ◦ ◦
Reaction and reporting − − • • − ◦ ◦ ◦
UEBA • • − • − • • −
Security • • − − ◦ ◦ ◦ −
−Low/Basic ◦Average •High/Advanced.
Correlation rules: The success of detecting an event by a SIEM relies on the power of the
correlation rules. While most SIEMs possess basic correlation rules, few of them have
robust search capabilities and support search processing languages to write complex
searches that can be used on the SIEM’s data.
Data sources: One of the key features of a SIEM system is the capacity for collecting
events from multiple and diverse data sources in the managed infrastructure. Most SIEMs
support several types of data sources natively, including both the supported sensors, and
the supported data types (e.g., threat intelligence). For other solutions (e.g., QRadar, USM)
such a feature could be supported by additional components integrated to the SIEM. This
feature evaluates the natively supported data sources and the possibility for a SIEM to
automatically customize them.
Real time processing: This feature considers the ability of a SIEM to handle real-time data
under constant change. It evaluates the real-time controls, monitoring, and pipelining
capabilities deployed by the tool in preventing or reacting to cybersecurity incidents, as
well as the performance computation capabilities that SIEMs have to analyze millions of
events in real time. All the studied SIEMs have advanced real time processing capabilities.
Data volume: Analyzing large volumes of data coming from different sources is impor-
tant to gain more insights from the collected events and to have a better monitoring.
However, keeping large volumes of collected data in a live SIEM system is often costly
Sensors 2021,21, 4759 7 of 28
and impractical. This feature evaluates the possibility of current systems to support large
volumes of data for correlation, indexing and storage operations.
Visualization: One of the key factors that hinder the analysis of security events is the
lack of support for proper data visualization methods and the little support provided
for interactive exploration of the collected data. It is therefore important to understand
the capabilities of the analyzed systems in terms of creation of new data visualization
methods and custom dashboards.
Data analytics: More recent versions of leading SIEMs support extensive integration with
application and user-based anomaly detectors. These capabilities include the analysis
of the behavior of employees, third-party contractors, and other collaborators of the
organization. For this, the SIEM must comprise the management of user/application
profiles and the use of machine learning techniques for detecting misbehavior.
Performance: This feature evaluates the performance of a SIEM solution in terms of com-
putational capacity, data storage capabilities (e.g., read/write), rule correlation processing
(e.g., high performance correlation engine), as well as data search, index, and monitoring.
Forensics: In addition to logging capabilities, some SIEMs (e.g., ArcSight, LogRhythm)
offer built-in network forensic capabilities that include full session packet captures from
network connections considered as malicious aiming at converting packet data into
documents, web pages, voice over IP, and other recognizable files. Some other products
(e.g., QRadar, Splunk) are able to save individual packets of interest when prompted by a
security analyst, but do not automatically save network sessions of interest [16], and the
rest of studied solutions have no built-in network forensic capabilities.
Complexity: SIEMs are known for being difficult to deploy and manage. However, it
is important to understand if the analyzed system can be installed for testing with low
or moderate effort. From the eight studied SIEMs, ArcSight is the tool with the highest
complexity for deployment and management, whereas LogRhythm and Splunk are seen
as easy and friendly tools to install, deploy, and use.
Scalability: This feature considers the ability for a SIEM deployment to grow not only
in terms of hardware, but also in terms of the number of security events collected at the
edge of the SIEM infrastructure. The new digital transformation leads to more sensors
and more devices (e.g., servers, agents, nodes) connected to the same network.
Risk analysis: Recent versions of leading SIEM systems (e.g., QRadar, LogRhythm, Splunk)
include features for doing risk analysis on the assets of the managed infrastructure. This
feature evaluates if the SIEM natively supports risk analysis or if it can be integrated with
external appliances for that purpose.
Storage: Considering that SIEMs generally store information for no more than 90 days,
this feature evaluates the length at which current SIEM technologies keep data stored in
their systems for further processing and forensics operations.
Price: This feature evaluates the licensing method associated to the SIEM solution (e.g.,
enterprise, free, beta, premium) and the limits in the number of users, queries, index
volumes, alerts, correlations, reports, dashboards, and automated remedial actions. Most
of the studied solutions are very expensive, except for LogRhythm, USM, and SolarWinds,
with more reasonable costs and the possibility to use open source solutions with more
limited capabilities.
Resilience: Resilience or fault tolerance is an important feature of any critical monitoring
system. It is important to understand what the fault tolerance capabilities of existing
SIEMs are, for example, if the correlation engine supports fault tolerance; the way disaster
recovery and replication are supported on the event storage; if the connectors support
high availability features.
Reaction and reporting capabilities: This feature studies the actions that are natively
supported by the SIEM to react against security incidents (including sharing and reporting
capabilities) and the way such actions are expressed to the correlation engine.
Sensors 2021,21, 4759 8 of 28
UEBA: This feature evaluates if the SIEM solution presents native User and Entity
Behavior Analytics (UEBA) capability, or if it provides integration with third-party
UEBA solutions.
Security: This feature evaluates the ability to implement security automation as well
as native encryption capabilities present in the SIEM during the monitoring, detection,
correlation, analysis, and presentation of the results.
4. Limitations of Current SIEMs
Even though the new generation of SIEMs provides powerful features in terms of
correlation, storage, visualization, and performance, as well as the ability to automate the
reaction process by selecting and deploying countermeasures [
65
,
66
], current response sys-
tems are very limited and countermeasures are selected and deployed without performing
a comprehensive impact analysis of attacks and response scenarios [67].
In addition, most SIEMs support the integration of new connectors or parsers to collect
events or data, and provide APIs or RESTful interfaces to collect the events at a later date.
These mechanisms allow creating add-ons and extensions to existing systems. Future
SIEMs must exploit this feature in order to enhance the quality of the events fed to the
system (e.g., using new monitoring systems or collecting external data from open source
intelligence) through custom connectors, and provide new visualization tools by collecting
data from the SIEM data repository.
This section details the main limitations found in current SIEM solutions and provides
some perspectives on possible enhancements.
4.1. Incomplete Data
Although current SIEMs deal with tons of data, none of them have all the data needed
to process and detect all security incidents. The reason is that it is not cost-effective to
capture and process all the required data. Typically, all SIEMs correlate logs from VPNs,
firewalls, domain controls, failed connections, etc. Most SIEMs are able to correlate logons,
malware, and web logs, but just few of the current SIEMs correlate DNS traffic, end-point
data logs, and email logs. As a result, it is not possible to know who everyone is in the
system [68].
Identity is fragmented, people possess shared accounts and different roles are as-
sociated to the same user, but by law, we cannot disclose the identity of a given person
since it generates privacy issues, as presented in the General Data Protection Regulation
(GDPR) [69]
. If the SIEM is not able to capture all data about users and high value assets,
correlation will never work properly, resulting in large number of false positives and nega-
tives. The next generation of SIEMs must, therefore, meet the privacy requirements of the
GDPR while providing enough information for analysts to identify security incidents [
70
].
The reviewing of existing SIEMs allowed us to confirm that these systems do not
provide high-level security risk metrics. A major advance on current SIEMs will be the
development of useful operational metrics that allow SOCs to make decisions supported
by quantitative evidence, where uncertainty in the measures is explicitly stated, and with
better visualization support to enable better communication of these decisions to the
relevant stakeholders in the organization [
71
,
72
]. Such measurements must be supported
on several layers of defense (e.g., firewalls, IDSs, anti-virus products, operating systems,
applications) and different products of each type.
Although cost sensitive metrics are hard to compute due to the difficulty in estimating
security costs of organizations, novel SIEMs must approach this category of metrics using
high-granularity estimation of costs.
Future SIEMs must explore and implement novel unsupervised techniques that com-
bine statistical and multi-criteria decision analysis to automatically model applications
and users’ behaviors, and subsequently identify anomalies and deviations from known
good behaviors that are statistically relevant. This will lead to the deployment of en-
hanced application monitoring sensors, which will feed SIEM systems with diverse types
Sensors 2021,21, 4759 9 of 28
of events that can be correlated with more traditional security events collected from host
and network-based appliances.
By combining anomaly-based events with those provided by more traditional heuristic
and signature based tools, SIEMs will improve the false positive rates of these compo-
nents, which have traditionally been the main stumbling block of their wide adoption in
real operations.
4.2. Basic Correlation Rules
SIEM platforms provide real-time analysis of security events generated by network
devices and applications [
3
]. These systems acquire high volumes of information from
heterogeneous sources and process them on the fly. Their deployment thus focuses, firstly,
on writing ad hoc collectors and translators to acquire information and normalize it, and
secondly, on writing correlation rules to aggregate the information and reduce the amount
of data. This operational focus leads SIEM implementers to prioritize syntax over semantics,
and to use correlation languages that are poor in terms of features [
73
]. However, as the
number of attacks, and thus the diversity of alerts received by SIEMs increases, the need
for appropriate treatment of these alerts has become essential.
Current SIEM correlation rules are weak [
74
]. Most of them use basic boolean chaining
of events that check for a specific attack path (one from the many thousands of possi-
bilities). Very few SIEM solutions have a built-in advanced correlation engine able to
perform the deviation and historical correlation useful for instance to check after zero-day
attack detection.
4.3. Basic Storage Capabilities
For most existing SIEM solutions, once data is archived and is out of the live system,
the SIEM will not use it. Moreover, how archived data is handled or where it is stored or
transferred is up to the user and is usually done manually. As there are diverse options
for where to store archived data some SIEM users will opt for attached storage, others will
use an in-house distributed file system, e.g., Hadoop Distributed File System (HDFS) [
75
],
a commercial cloud storage solution like Amazon S3, Amazon Glacier, or even use “scp”
operations to another device.
Regardless of the archiving solution employed, the actual archiving process consists
of running scripts that are often custom built for a specific IT environment. Therefore, a
script used by one customer may not be useful for another customer’s need, and a change
in the archive option requires rewriting the archiving script.
Furthermore, archiving retired data from a SIEM can be costly and can pose security
and reliability problems if the archived data is not handled correctly.
Current infrastructures usually store raw events for a limited amount of time (e.g.,
6 months
) to limit the storage space used for such archival (e.g., 6 TB). Given that some ad-
vanced persistent threats are detected many months after their inception in the
system [76]
,
such storage capabilities might be insufficient to help with certain incidents.
Although promising, most companies avoid using the cloud due to concerns related
to the confidentiality of the events (that contain sensitive information [
69
]) and concerns
related to trusting such important data to third parties [77].
The goal of future SIEMs must focus on offering a secure and elastic solution for data
archival regardless of the data retention needs with the ability to customize policies to fit
retention requirements.
4.4. Reliance on Humans
Research in SIEM technologies has traditionally focused on providing a comprehensive
interpretation of threats, in particular to evaluate their importance and prioritize responses
accordingly. However, in many cases, threat responses still require humans to carry out
the analysis and make decisions with respect to understanding the threats, defining the
appropriate countermeasures, and deploying them. This is a slow and costly process,
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requiring a high level of expertise, and remains error prone nonetheless. Thus, recent
research in SIEM technologies has focused on the ability to automate the process of selecting
and deploying countermeasures.
According to Scarfone [
78
] automated reactions must consider (i) time-line: the time
that a SIEM takes to detect an attack and direct the appropriate security control to mitigate
it; (ii) security: the communications between the SIEM and the other security controls
protected so as to prevent eavesdropping and alteration; (iii) effectiveness: the ability for a
SIEM product to stop attacks before damage occurs.
4.5. Basic Reaction and Reporting Capabilities
Traditionally, SIEMs support the creation of security directives for detecting suspect
behavior in the system and reporting alarms. However, these directives/rules could,
in principle, be used to trigger actions for modifying the managed infrastructure (e.g.,
changing the configuration of firewalls or NIDS).
For some SIEMs, it is possible to use automatic triggers to perform external actions
(e.g., send emails, execute scripts, open tickets), usually through a command line. However,
most of these systems do not provide pre-configured and customized actions to be triggered
when a specific condition or set of conditions are fulfilled. They generally focus on the
creation, distribution, and management of reports.
In addition, some SIEMs require the use of additional solutions (e.g., add-ons, appli-
ances, extensions) to provide automatic reactions when an alarm is detected.
An important part of the design for security is defense in depth [
79
] using layers of
defense that reduce the probability of a successful attack (or at least contain its effects). This
requires the use of diversity, including but not limited to the use of multiple intrusion detec-
tion systems (IDSs) and disparate open-source intelligence data (e.g., infrastructure-related
information about security from open-source intelligence data available on diverse sources
from the internet). There has been only sparse research on how to choose among alternative
layered defenses; occasionally, unsuitable models appear relying on the naive assumption
of independent failures between the diverse components [
80
]. Security engineers have
little or no theory to guide their decisions about diversity, although unaided intuition can
be very misleading (e.g., Littlewood and Wright [81]).
SIEMs already provide the functionality for reading logs from multiple different
security monitors and detection tools at different layers. Future SIEMs should build tools
that allow consolidation of outputs from multiple diverse monitors of similar type, which
may be monitoring similar types of assets. This will help in improving the accuracy of the
detection, and reducing the false alarm rates that are reported back to the SOCs.
Even though the need and relevance for providers of security services having Cyber
Security Reporting Systems (CSRS) was identified almost two decades ago [
82
], there is
still a lack of solutions focused on the management and generation of mandatory incident
reporting according to different regulatory frameworks. In addition, although the growing
quantity of existing regulations and legislation addressing cybersecurity incidents has
created a need for studies on cybersecurity incident reporting for specific areas (e.g.,
nuclear facilities [
83
], safety-critical ystems [
84
]), currently this functionality is very limited
in most commercial and open-source SIEMs. Solutions such as IBM QRadar, AT&T USM
anywhere, or Splunk generate reports about detected security incidents, nonetheless, such
reports do not follow standards or common templates, and the information included does
not cover what is required for mandatory incident reporting to the different supervisory
authorities [85].
4.6. Limited Data Visualization
During the reviewing of the state-of-the-art of existing SIEMs, we observed that the
reporting and data visualization capabilities are limited in terms of supporting the effective
extraction of actionable insights from the huge amount of data being collected by the
systems. Although all SIEMs offer data visualization capacities to their users, most often
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the visual representations are generic, not designed with particular user needs in mind, or
even are too highly rudimentary to have any significant effect on how the generated data
is utilized [86].
In addition, existing systems do not have the capacity to use diverse data modes, e.g.,
statistical modeling outputs, OSINT data collections, or comprehensive models of user
behavior. These novel data facets, when combined with the data already being gathered,
offer challenges and opportunities for a new generation of SIEMs.
To enhance the visualization capability of existing systems, SIEMs must focus on
flexible platforms able to work with several data sources that carry heterogeneous char-
acteristics and with data that is under constant change, i.e., real-time streaming data. In
addition, visualization must enable security analysts to better profile the system with
novel representations that communicate the provenance of an attack, ongoing activities,
vulnerabilities, and the characterization of sessions/users [87,88].
5. The Future of SIEMs
The changing nature of security threats, the proliferation of mobile devices, global-
ization, the explosion of social media, and quick changes in regulation are speeding the
evolution of Security Information and Event Management. The purpose of this section is to
analyze the external factors that could potentially affect the future of SIEM systems and
their related technologies in the mid-term and long-term based on political, economical, so-
cietal, technological, legal, and environmental factors [
89
–
91
]. We employ the
PESTLE [92]
analysis aiming at identifying the enablers and/or barriers that could directly or indirectly
affect the evolution of SIEMs.
5.1. Political Factors
Protection of individual properties and sensitive business or personal information
in the cyberspace is becoming critical and political organizations must take part in this.
They must design the security framework, principles, and rules to reduce the risks in the
population. This risk may economically affect private companies and public institutions.
These regulations may affect the evolution of SIEMs in the future, since, in some instance,
they analyze sensitive information to detect security events in the network.
Recently the EU Commission announced an increase (expected to trigger EUR 1.8 bil-
lion of investment by 2020) in the investment on cybersecurity in order to put more efforts
to reduce cyber-threats in the European Union [
93
]. In addition, according to Andrus
Ansip, Vice-President for the Digital Single Market, without trust and security, there can be
no Digital Single Market. Europe is proposing concrete measures to strengthen resilience
against cyber-attacks and secure the capacity needed for building and expanding the
digital economy. Furthermore, Gunther H. Oettinger, Commissioner for the Digital Econ-
omy and Society, considers that Europe needs high quality, affordable, and interoperable
cybersecurity products and services [93].
This is an initiative of the Commission to establish contractual Public Private Part-
nership [
94
] (cPPP) on cybersecurity between the European Union and the European
Cybersecurity Organization. The adoption and evolution of SIEMs can then be empowered
by this investment in cybersecurity.
5.2. Economic Factors
Among the economic factors that will affect the future of SIEMs the following can
be highlighted:
•
Short term/temporary work. In 2014 the main type of employment relationship in the
EU was full-time permanent contracts, with 59% of the share of employment, although
this is decreasing while the share of non-standard forms of work is increasing. If this
trend continues, it may well become the case that standard contracts will only apply
to a minority of workers within the next decade [
95
]. Due to the new types of work,
tending to shorter term jobs, people do not stay in the same company for a long time,
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especially in the first period of their career. The consequence is that companies need
to minimize the employee’s ramp up to learn a new tool, or a new way of working.
Therefore, this factor makes it essential that future SIEMs have improved and more
friendly interfaces at the level of decision taking, configuration rules, links to new
sources, and sensors.
•
Freelance. Self-employment is increasing against the usual company paid employ-
ment [
96
]. Freelancers do not work for a company as an employee but as a service
provider. This type of work may be a threat for companies because the devices used
by freelancers do not belong to the IT department and cannot be easily monitored.
Furthermore, they do not have strong bonds with the company that hires their services.
However, freelance cybersecurity consultants can be a good choice for SIEM providers
because they may possess a wider knowledge about potential threats affecting an
organization, since they accumulate a lot of experience from different companies.
•
Cyber security jobs are continuously growing. The estimated growth in cybersecurity
jobs is of 35% by 2020 [
97
]. This reflects the importance of cybersecurity for the
companies, and that can be an opportunity for SIEMs to grow in the market.
•
Bigger companies, globalization. The global market makes it easier for big techno-
logical companies to survive and grow more [
98
]. However, the level of criticality of
that information may be higher. Future SIEMs should be dimensioned for such big
companies and global networks.
•
Small and medium sized enterprises. SMEs will become bigger targets of cyber-attacks
in the future [
99
]. They should be the new target for SIEM market growth, making
models like SIEM as a service more attractive to SMEs.
5.3. Societal Factors
Society is becoming strongly dependent on information and communications technol-
ogy (ICT), which is leading to a rapid social, economic, and governmental development.
The following introduces how the changes in societal habits related to technology will
affect the future of SIEMs.
•
Generation Z. Modern generations understand the world as a big network in which
everything is connected to the internet. It can be assumed that people of the future
will be more aware of cybersecurity and will bring companies clearer awareness of
the risks associated to threats in the network [100].
•
Growth of social networks. There is a huge growth of social networks usage among
the young generations in the last few years. Social network activity is a source of
data that should not be disregarded, and it can be of very high importance in security
events analysis [101].
•
Cyber-attacks. In the new connected societies, the development of the internet has
led to a new type of attacks, i.e., cyber-attacks. Attacks to critical infrastructures can
be considered the new weapons, which makes SIEMs essential in any infrastructure
in which data is of relevance or whose attack may cause operation disruption, even
damage to population, not only from a single company’s perspective but also from
users, citizens, and (more generally) people’s perspective [102].
•
Deep web. The deep web is the part of the World Wide Web whose contents are not
indexed by standard search engines [
103
]. This can be considered as a barrier by SIEM
systems, since it makes it difficult to retrieve data from the network.
5.4. Technological Factors
Among the technological trends that will affect the way SIEMs evolve in the future,
the following can be highlighted:
•
Cloud storage. This technology can be clearly seen as an enabler in SIEM technology
since big data analytics of network events can be performed in a more efficient way,
without worries about the amount of logs, information, etc., that are stored.
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•
Cloud service integration. This is treated separately to cloud storage because it is
more focused on executing software in a remote server, and not only keeping data
“statically” in a cloud infrastructure. This technology makes it possible to ensure
scalability and high availability of software applications since they are not restricted
to the hardware of a local server, and can be launched from anywhere.
•
Mobile technologies. The growth of mobile devices brings new threats that should be
analyzed by SIEM systems. In this respect, it is a trend that employees use company-
owned devices as well as personal devices for office work. A need would be to
secure corporate data. Working at home, e.g., with a personal computer, what now is
commonly called BYOD (Bring Your Own Device), is a trend in cybersecurity [
104
].
However, this leads to several potential problems: BYOD devices are not managed by
the IT team so they are not under the policy control of the company; some BYODs do
not have any security solution pre-installed; data in these devices is not encrypted;
applications installed in those devices cannot be tracked.
•
Big data analytics. As introduced before, SIEMs are evolving to data analytics systems.
Data in a connected environment grows exponentially and makes it necessary to
have powerful analysis tools capable of real time analysis of events, support to
decision making, etc. The growth in data analytics methods is clearly an enabler
for SIEM systems.
•
Machine learning technologies. New high performance computers, with powerful
hardware and modern programmatic languages, together with the data analytics
explained above, are making it possible to create data models fed by the experience of
cause-effect analysis. SIEMs can take advantage of these technologies to make event
detection and decision making smarter [105].
•
Internet of Everything. The Internet of Everything (IoE) [
106
] is a ubiquitous commu-
nication network that effectively captures, manages and leverages data from billions
of real-life objects and physical activities. It extends the concept of Internet of Things
(IoT) by also including people, processes, locations, and more. The impact of this
technology on SIEMs is that they provide large amount of data and events for analysis.
•
5G Networks. 5G represents the next generation of communication networks and
services, an approach for fulfilling the requirements of future applications and scenar-
ios. This technology will increase the data transfer speed, and then could affect the
amount of data analyzed by a SIEM in a network per time unit. This can impose a
difficulty for SIEMs in events detection.
•
Social media analytics. Social networks like Twitter provide a wealth of information
that may be explored by cybersecurity companies as well as by hackers, as attack
victims use on-line social media to discuss their experience and knowledge about
attacks, vulnerabilities, and exploits.
5.5. Legal Factors
In January 2012, the European Commission proposed a comprehensive reform of
data protection rules in the EU. On 4 May 2016, the official texts of the Regulation and the
Directive REGULATION (EU) 2016/679 were published in the EU Official
Journal [107]
.
While the Regulation entered into force on 24 May 2016, it was set to apply from 25 May
2018. The EU Member States had to transpose the directive into their national law by 6
May 2018.
The objective of this new set of rules is to give back citizens the control over their
personal data, and to simplify the regulatory environment for business. The data protection
reform is a key enabler of the Digital Single Market which the Commission has prioritized.
The reform will allow European citizens and businesses to fully benefit from the digital
economy [108].
A number of provisions of the Directive contain a substantial degree of flexibility in
order to find an appropriate balance between protections of the data subject’s rights on the
one side and on the other side the legitimate interests of data controllers [109].
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In order to understand how this regulation may affect the data collected by SIEMs,
we can see for example how EC understands the propriety of the IP address in a network
(commonly analyzed by security software). In the internet, every computer is identified
by a single numerical IP address of the form A.B.C.D. where A, B, C, and D are numbers
in the range of 0 to 255. The working party has considered IP addresses as data relating
to an identifiable person, especially in those cases where the processing of IP addresses
is carried out with the purpose of identifying the users of the computer (for instance, by
copyright holders in order to prosecute computer users for violation of intellectual property
rights), the controller anticipates that the “means likely reasonably to be used” to identify
the persons will be available, for example, through the courts appealed to (otherwise the
collection of the information makes no sense), and therefore the information should be
considered as personal data [110].
Consequently, the way SIEMs process and store data must be in line with the directives
on data protection. Moreover, the regulation in data protection affects the SIEMs in the
way they can store the data, where the database is located, and that the stored data is kept
with adequate level of protection.
5.6. Environmental Factors
SIEM challenges will continue to evolve as security managers grapple with cloud
services, mobile, the Internet of Things, and other new technologies the IT department
does not always control. IoT will be a huge factor as it drives the number of endpoints
vulnerable to attackers [
111
,
112
]. It gets harder for cybercriminals to infiltrate computers
but is still fairly easy to hack cameras, refrigerators, microwaves, Bluetooth tools, and other
connected devices and use them as an attack vector.
The growth of cloud, especially for small and medium businesses (SMBs), has trans-
formed how businesses store and handle data. Companies once intimidated by the high
price of data storage, benefit from SIEM providers like ArcSight, Nitro, and others that
deploy modules from the cloud [111].
Cloud services and IoT devices will rapidly generate increasing amounts of data,
and SIEM systems will have to adapt by learning to collect and organize the influx
of information.
6. Potential Enhancements of Future SIEMs
SIEMs are mostly used in IT infrastructures where automated detection and reaction
is possible. However, in critical infrastructures, these tools require manual intervention
and in-depth analysis of events before implementing a security countermeasure. This
section provides potential enhancements on the future generation of SIEMs considering
the following aspects:
6.1. Diverse Security
Enhancing SIEMS with diversity-related technologies provides a major improvement
of current solutions. Special attention must be paid to diversity measures - i.e., how similar
or different security protection systems, vulnerabilities, attacks, etc., are from each other.
These types of diversity metrics are less studied in the literature compared with metrics for
individual components.
Future SIEMs must define security metrics that consider quantitative and probabilistic
methods to support decisions on how best to combine multiple defenses given a threat
environment [
113
,
114
]. This involves understanding how the strengths and weaknesses of
diverse defenses add up to the total strength of the system.
The security community is aware of diversity as potentially valuable [
115
]. The
literature touches on the use of ensemble methods to assess the results of classification
systems for security [
116
]; however, SIEMs should focus in diverse inputs rather than the
aggregation of diverse machine learning techniques.
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6.2. OSINT Data Fusion
A potential enhancement for current SIEMs could be the use of language processing to
identify threats from the use of keywords that typically indicate a threat in major languages;
such as “ddos”, “security breach”, “leak”, and more [
117
–
119
]. This information can be
used to tag OSINT data as relevant or irrelevant. In addition to the type of threat, other
information from the OSINT sources such as location and entities involved could also
be extracted to provide a more comprehensive description of the threat. The prediction
confidence of the classifier can be included in the data sent to SIEMs, which will help to
avoid the issue of false alarms.
6.3. Enhanced Visualisation
To enhance the visualization capability of existing SIEMs, we identify the following
improvements [86]:
•
Design and develop a rich set of specialized visualization models that handle diverse
types of data e.g., high-dimensional, temporal, textual, relational, spatial.
•
Provide effective overviews, interactive capabilities to focus on details, and mecha-
nisms to compare individual and/or groups of data instances.
•
Design and develop visualization models capable of handling the dynamic nature of
the data (e.g., streaming system activity logs, OSINT data, etc.) to support real-time
analysis and decision-making.
•
Develop a visual summary of user activities that reveals common/abnormal patterns
in a large set of user sessions, compares multiple sessions of interest, and investigates
in depth of individual sessions.
6.4. Enhanced Storage
In addition, archiving retired data from a SIEM can be costly and can pose security
and reliability problems if archived data is not handled correctly. A potential solution for
these issues could be to develop a SIEM extension that handles data archiving in a reliable,
flexible, and secure manner leveraging public Clouds (e.g., Amazon S3, Amazon Glacier,
Windows Azure, Blob Store, etc.). The goal is to offer a secure and elastic solution for SIEM
data archival regardless of the data retention needs with the ability to customize policies to
fit retention requirements [120].
6.5. Integration with Security Orchestration Automation and Response (SOAR)
SOAR refers to three main security topics: (i) security orchestration, focusing on the
workflow management, integration and unification of components involved in security
operations; (ii) security automation, responsible for automating repetitive controls, tasks
and processes taking place in security operations; (iii) security incident response, focusing
on the identification and management of security threats and incidents. SOAR solutions
would ideally complement the capabilities of current SIEMs, which together with addi-
tional technologies such as Threat Intelligent Platforms (TIPs) [
121
], Endpoint Detection
and Response (EDR) [
122
], or Next-Generation Firewalls (NGFW) [
123
] are seen as a proac-
tive platform for early detection, prevention, and response of cybersecurity threats and
attacks [124–126].
The next generation of SIEMs must integrate evolved and adaptive SOAR solu-
tions with advanced capabilities that enable dynamic interactions at all phases of the
incident workflow to quickly deal with existing and emerging threats [
127
,
128
]. Exam-
ples of enriched adaptive SOAR include the NextGuard Adaptive security Operations
suite from Nokia NextGuard (https://www.nokia.com/networks/solutions/netguard-
adaptive-security-operations/ accessed on 7 June 2021), the Splunk adaptive Operations
Framework (AOF) (https://www.splunk.com/en_us/solutions/solution-areas/security-
and-fraud/adaptive-response-initiative.html accessed on 7 June 2021), and the Integrated
Adaptive Cyber Defense (IACD) (https://www.iacdautomate.org/ accessed on
27 May 2021)
.
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6.6. AI/ML Capabilities
In order to improve detection, correlation and reaction capabilities, the next generation
of SIEMs should integrate AI/ML technologies in their core engines [
129
]. AI technolo-
gies in SIEMs offer predictive capabilities particularly useful for the analysis of abnormal
behavior of network traffic, tools, and users. Few of the current SIEM solutions (e.g., Lo-
gRhythm NextGen SIEM Platform (https://logrhythm.com/products/features/ai-engine/
accessed on 28 May 2021), QRadar SIEM (https://www.midlandinfosys.com/ibm-power/
all-categories/ai-security-siem-qradar-uba.html accessed on 28 May 2021)) use machine
learning (ML) to learn about threats as they acquire threat intelligence and deflect attacks
in the filed [130,131].
One step forward for cyber threat detection, mitigation, and prevention is to consider
AI/ML in SOAR solutions which would be ideally integrated in SIEM platforms. AI/ML-
powered defense systems are able to analyze large amount of data and identify suspicious
patterns in real-time (or near real-time). The main targets for AI/ML applications include
intrusion detection (network-based attacks), phishing and spam (emails), threat detection
and characterization, and user behavioral analytics [132].
AI-based SIEMs are able to make decisions and/or change their behavior accord-
ingly, which improves detection capabilities by discovering more blind spots, reduces
dependencies of manual intervention, as some reactions can be automated, and minimizes
false positive rates, as algorithms have the ability to accurately classify data as normal or
abnormal. Ideally, next-generation SIEMs should combine rule-based analysis with the one
provided by AI technologies to detect users deviations, identify changes in users activity
vs. frequency, detect anomalous deviations from peer groups, prioritize users and assets,
and respond to threats quickly and accurately [133].
Improvements of future SIEMs should also include creating sensors that rely on un-
supervised statistical learning approaches to firstly create a baseline for normal entity
behavior (users and applications alike). The scope is to be able to highlight anomalies
and/or deviations from this pattern by using a SIEM scoring-alerting system . In terms of
User Behavior Analysis (UBA), a set outlier detectors or classifiers as well as other unsu-
pervised machine learning algorithms could be used in order to manage user/application
profile [134–136].
6.7. Other Potential Enhancements
The review of existing SIEMs revealed that these systems do not provide high-level
security risk metrics. The next generation of SIEMs must pursue the development of
risk-based metrics considering several layers of dependencies such as hosts, applications,
middleware, and services. These will allow scoring the risk for the different operational and
functional areas. Attack propagation and attack impact metrics [
137
] could be extended
to consider different hierarchical operational layers. Though cost metrics can be hard to
compute due to the difficulty of organizations in estimating security costs, one potential
enhancement is to approach this category of metrics using high granularity estimation of
costs to define acceptable thresholds [138,139].
In addition, considering the fact that 5G and/or IoT technologies are expected to affect
current SIEM architectures due to the increased volume of data to be processed, it will be
necessary to move towards a hierarchy of SIEMs and create collaborative mechanisms that
will help notify and manage relevant security incidents. In the 5G domain, for instance,
a SIEM solution is currently able to cover the analysis of one network slice; however, in
the near future we will require collaboration mechanisms among multiple slices. Such a
mechanism can be particularly useful in architectures where detection is required to be
performed closer to the edge. In the IoT domain, for instance, having several SIEM systems
working in different layers (e.g., SIEMs deployed in gateways) could be of great interest.
These SIEMs must be lighter and more domain-specific than current solutions.
Furthermore, integration of SIEMs with extended detection and response (XDR)
platforms is expected to provide value in two different but complementary ways: (i) having
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SIEMs focused on compliance and evolving to serve as a broader threat and operation risk
platform, and (ii) having XDR focused on threats and providing a platform for deep and
narrower threat detection and response. As a result, organizations would require solutions
providing detailed level of information about the network and/or user activity taking place
in the cloud or locally, to detect threats more accurately [140].
Finally, considering that the use of SIEMs generally require SOC operators and that
current infrastructures are more diverse and dynamic, the next generation of SIEMs must
focus on providing more autonomy and less effort in its deployment and management,
which in turn will decrease their cost by simplifying their usage and operation.
7. SIEMs in Critical Infrastructures
Critical infrastructures (CIs) are organizational and physical structures whose failure
and/or degradation could result in significant disruption of public safety and security. They
rely on the Supervisory Control And Data Acquisition (SCADA) technology to monitor
industrial and complex systems based on Networked Control Systems (NCSs). CIs include
sectors that account for substantial portions of national income and employment (e.g.,
energy, water, transport, finance, health, etc.). Most of them use Industrial Control Systems
(ICS) to provide control of remote equipment (using typically one communication channel
per remote station) [141–143].
Security in computer networks must be distinguished from security in critical infras-
tructure networks, since the interactions among nodes in CI networks is done in real time
at a physical level. A great effort has been dedicated on the usage and implementation
of cybersecurity solutions in the protection of CI networks. Nevertheless, most of the
current approaches used in the cyber domain are neither suited nor feasible to be imple-
mented in the CI domain, making it a big challenge when it comes to protecting CIs against
cybersecurity threats [144].
A key objective on protecting critical infrastructures is improving their security, which
involves not only enhancing physical security, (e.g., ensuring physical rooms are locked
appropriately to prevent access from unauthorized people), but also implementing effective
cybersecurity measures to reduce the attack surface. Although a great effort has been made
on the protection of CIs against cyber-attacks, they still present significant challenges e.g.,
it is not possible to execute a vulnerability scanning on an ICS as it is done in virtual
systems since it may take the industrial system offline and thus, could bring down a plant’s
operations [145].
While classical IT networks focus more on confidentiality and integrity (ensuring
data is protected), ICS focuses more on availability (ensuring the system is always up and
running). Industrial systems were not designed with security in mind, they were designed
simply to be operative. They are generally legacy systems running on older operating
systems, typically unpatched, and fragile in many cases. Although security strategies (e.g.,
network segmentation, firewalls, physical air-gaps, endpoint security, etc.) are deployed to
decrease risk levels, they sometimes foster a false sense of security. Malicious entities can
exploit gaps in corporate networks and move laterally into industrial systems to steal data
or damage critical assets [146].
Security administrators require not only the collection of huge amounts of data, but
also finding connections among these data in a way that can help identifying potential
threats as well as defining appropriate mitigation strategies. Although this process has
traditionally been performed through SIEM systems, current solutions are not able to fully
detect all types of attacks affecting critical infrastructures [
146
]. In addition, considering the
fact that attacks have increased both in number and complexity, organizations are obliged
to improve their security by using tools with more advanced capabilities for the protection,
detection, and reaction against cyber and physical attacks. SIEM systems are definitely an
interesting solution to cope with these challenges. They are rapidly advancing into data
analytic platforms that provide high-performance correlation functionalities and are able to
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raise alerts from a business perspective considering different alert aggregation
methods [5]
and events collected at different layers in real time [147–149].
The remainder of this section provides examples on the usage of SIEMs solutions in
different industrial sectors.
7.1. Energy Distribution
The energy sector (including the production, storage, transportation, and refining of
electrical power, gas, and oil) is particularly affected by cybersecurity threats and attacks.
According to a recent study, three main aspects make of this sector vulnerable to cyber-
attacks: (i) the increased number of threats and actors targeting utilities; (ii) the increased
attack surface, arising from their geographic and organizational complexity; (iii) the unique
interdependencies between physical and cyber infrastructure in the electric-power and gas
sectors. As a result, energy companies are vulnerable to a wide range of threats including
billing fraud with wireless “smart meters” and even physical destruction [150].
SIEMs are being considered as an essential solution to protect the energy industry
against a variety of threat scenarios. A research study [
151
] performed on power grid
infrastructures evaluated the benefits of SIEM solutions in detecting attacks (e.g., sleep
deprivation, distributed denial of service, GPS spoofing). The SIEM technology used in this
domain is able to perform techniques to monitor absolute and relative signal strengths and
compare received ones against expected ones in order to identify anomalies in power grid
infrastructures. As a result, an alarm is raised whenever a deviation is found, and valuable
information is provided to the security analyst in order to mitigate and manage detected
attacks. Thus, the use of SIEM technologies is proven to be beneficial in the protection of
critical assets.
7.2. Water Supply
The water sector is also affected by cyber-attacks. Threat actors can attack water at
its source, treatment plants, storage facilities, or distribution centers. SIEM solutions help
monitor the entire SCADA network in real time to respond to any changes in the quality
of water as soon as they are detected, as they might represent a potential attack. Current
version of SIEMs such as the LogRhythm NextGen SIEM Platform [
152
] allow security
administrators to effectively observe, collect, and analyze the data from the data historian
in one interface, as well as identify any deviations from the acceptable ranges (e.g., for
drinking water) during a specific period of time. Examples of attacks that can be detected
by correlating security events in the industrial control network include reconnaissance,
network behavior changes, changes in operator or engineering user behavior, detected or
failed malwares, web-based attacks targeting human machine interfaces (HMI), man-in-
the-middle attacks, etc.
One of the major challenges the water sector faces is the lack of cybersecurity situ-
ational awareness and the gaps in defense in depth mechanisms. The common belief in
many sectors is that a high level of security can be achieved by deploying cutting edge
technologies to protect and counter potential risks. However, defense in depth cannot
be achieved if organizations do not clearly understand the relationship of vulnerabilities,
threats and the mitigation measures used to protect the operations, personnel, and technolo-
gies of an ICS. Defense-in-depth is a holistic approach that considers the interconnections
and dependencies among the aforementioned entities while protecting the organization’s
assets and using their available resources to provide effective layers of monitoring and
protection based on the business’s exposure to cybersecurity risks [153].
Next-generation SIEMs must enable multiple technologies to work together over IT
and OT environments instead of operating in silos, so that organizations obtain automated
responses to security incidents more quickly, have a complete visibility of their networks,
and are able to plug OT security gaps as well as simplified management. Fortinet (https:
//www.fortinet.com/resources/cyberglossary/critical-infrastructure-protection accessed
on 1 April 2021), is an example of such tools that offer protection for SCADA systems
Sensors 2021,21, 4759 19 of 28
and ICS while enabling organizations to design the security of their infrastructures more
efficiently and in compliance with current laws and regulations.
7.3. Transportation
Transport networks have become increasingly digitized, with a wide range of data
flowing across systems, tracking and monitoring both digital and physical networks. As
more devices and control systems are connected online, more vulnerabilities will appear,
increasing the potential for disruption to physical assets. Threat actors can attack all
transportation modes including aviation services, highway and motor carriers, maritime
transport systems, and railway services [154].
As cyber technology becomes more sophisticated, the threat from attack is moving
from data breaches to interrupting physical critical infrastructure, exposing transport
operators to economic and reputational damage. Some of the key cyber risks affecting
the transportation industry include physical asset damage and associated loss of use,
unavailability of IT systems and networks, loss or deletion of data, data corruption or loss
of data integrity, data breach, cyber espionage, extortion, theft, and damaged reputations.
Most of these risks are realized through the exploitation of vulnerabilities that use social
engineering techniques to deliver spam and phishing campaigns, inducing virus and
malware installation (including ransomware) [155].
SIEM systems are essential in the improvement of the cyber and physical security
in all transport services. Several solutions [
156
–
158
] have been proposed in the literature
to protect various transport modes in the EU. As a result, it has been possible to develop
cybersecurity plans aligned with the infrastructure’s overall strategy, to improve security
in systems and applications, to have cybersecurity support for new developments, to raise
employees’ awareness, to permanently manage security in both a preventative and reactive
way, and to apply clearly defined security policies.
7.4. Healthcare
As medical procedures, diagnostics, and health data are becoming electronic, cloud-
based, and distributed among numerous stakeholders, healthcare infrastructures have
gained the attention of potential malicious third parties. According to an IBM
survey [159]
,
ransomware (or any kind of malware), social engineering (e.g., spear phishing), and
bad practices adopted by staff and clients alike are the most common attack vectors in
the sector.
In the near future, for the patient-centered healthcare model to fully function, sharing
medical data and information between stakeholders and healthcare service providers is
inevitable. The individualized patient approach, mobility, increased usage of personal
medical devices, and commercial personal healthcare devices are making the roles of
these devices and usage of their data even more indistinct. Technology and threats keep
developing and only secure-by-design medical devices and services should be approved to
healthcare networks. Nevertheless, there will be new cyber-attacks and new unknown vul-
nerabilities and threats, which is why the use of technologies (e.g., SIEM) is
essential [160].
According to a recent study [
161
], the features that make SIEM solutions essential to be
used by healthcare organizations are: (i) real-time analytics; (ii) self-learning configuration
management database; (iii) scalabale log management; (iv) multi-tenant management;
(v) compliance reports. SIEM solutions provide security administrators a consolidated
and global look into organization’s security events which can prevent Health Insurance
Portability and Accountability Act (HIPAA) violations and keep health data safe. While
components of the healthcare infrastructure have their own security features, the ability to
see all events in one dashboard is invaluable to protect data [162].
Sensors 2021,21, 4759 20 of 28
7.5. Financial Services
Financial organizations represent a major target for external and insider threats seek-
ing financial gains or rewards. The major challenges faced by financial enterprises are
three-fold: (i) business scaling, which exposes the sector to more potential attack vectors as
the data is managed by third-parties through the use of clouds; (ii) legal and regulatory
compliance, which restricts the use of personal identifiable data and requires the imple-
mentation of technologies according to privacy standards; (iii) insider threats (current or
former), which generally go undetected and can cause serious harm to the business either
out of ignorance or intentionally [163].
In terms of legal and regulatory compliance, a major challenge is related to a manda-
tory incident reporting to the competent and supervisor authorities and the need to compile
information about incidents to generate and share reports that in many cases must be com-
pliant with diverse regulations, procedures, templates, data sets, and other requirements.
Although reporting is one of the key steps always present whenever a security incident
takes place, there is not an agreement or a common procedure to be followed for incident
reporting and sharing, even in the same sector such as the financial one. As a result, the
lack of standards generates unstructured reports that cannot be easily analyzed. Key-search
automated approaches for data extraction cannot be applied because they produce a high
number of false associations in the analyzed reports. SIEMs must improve and simplify
the process of collection and mandatory reporting and sharing of the information about
major security incidents suffered by the financial institutions [85].
Modern SIEMs features User and Entity Behavioral Analysis (UEBA) to identify base-
line behaviors of users, devices, and applications. Insider threats are therefore detected
as soon as a user violates their baseline behaviors. In addition, SIEM solutions can detect
data exfiltration through unusual network traffic and/or abnormal usage of internal re-
sources by outsiders. SIEM solutions can also help financial enterprises achieve compliance
through out-of-the-box reports and automatic report filling [
164
]. Other usages of SIEMs in
the financial sector include account abuse (e.g., detect and respond to employees checking
on dormant customer accounts), audit trial protection from unauthorized manipulation,
forensics, and fraud detection [165].
8. Related Work
The analysis and evaluation of security systems have been widely proposed in the
literature. While some research focuses on the commercial aspects, others concentrate
on the technical features that could be improved in current SIEM solutions. Well known
institutions like Gartner [
20
], for instance, propose a commercial analysis of SIEM systems
based on the market and major vendors, for which a report is released on an annual basis
to position SIEM vendors as market leaders, challengers, niche players, or visionaries.
Although companies like Gartner periodically evaluate the capability of SIEMs, to the best
of our knowledge, there is no systematic survey of these systems, their capabilities, and the
open gaps.
In addition, other security institutions (e.g., Techtarget [
166
], Info-Tech Research
Group [167]
), have widely reported on the capabilities of SIEM solutions and on the way
SIEM vendors can be compared and assessed. Techtarget, on the one hand, releases pe-
riodic electronic guides about securing SIEM systems and how to define SIEM strategy,
management, and success in the enterprise [
168
]. Info-Tech, on the other hand, provides
technical reports on the SIEM vendor landscape [
169
] focusing on the benefits and draw-
backs of major commercial SIEMs. Both organizations take the Gartner Magic Quadrant as
the baseline for their analysis, leaving aside the more technical aspects to be considered in
future SIEMs.
Similarly, organizations such as Solutions Review [
22
] offer periodic reports to guide
SIEM buyers on the appropriate selection of the SIEM solution for their businesses. Authors
analyze key SIEM capabilities and perform a comparison vendor map based on three
fundamental aspects (i.e., compliance, log management, and threat detection). Although
Sensors 2021,21, 4759 21 of 28
the report allows connecting potential buyers with vendors, it does not provide technical
details of the tools nor discusses about potential capabilities to be enhanced in current
SIEMs, or external factors that could affect their performance in the future.
Caccia et al. [
68
], provide an analysis on the future of SIEMs by discussing aspects
such as limitations of current SIEMs, the need for improvements in SIEM features, and
the use of User and Entity Behavior Analytics (UEBA) for effective detection and efficient
response. The authors focus on technical features to be enhanced in current SIEM solutions,
but no details are given on the potential barriers and enablers to be considered in the
development and implementation of future SIEMs.
Kotenko and Chechulin [
170
] propose a framework for attack modeling and security
evaluation in SIEM systems applicable for future systems of the Internet of Things. The
approach concentrates on technical features (e.g., evaluating the usage of comprehensive
internal security repository, open security database, service dependency graphs, attack
graphs, and security metrics) to be integrated into a SIEM framework in order to enhance its
functionality. As a result, the authors claim to achieve more accurate and faster evaluations
of network security aspects by the use of the proposed attack modeling and security
evaluation component. Besides some technical aspects, no other features are considered for
the improvements of current SIEM systems.
Based on the aforementioned limitations, we propose in this paper an analysis of
current SIEM solutions based on commercial and technical features that could lead to
enhancements in the design, development, and implementation of the next generation of
SIEMs. The analysis focuses on the limitations of current SIEMs and on external factors
that could potentially affect them in the long term. It includes a review and comparison of
different commercial SIEMs during the last decade.
9. Conclusions
This paper presents a commercial and technical analysis of some of the leading SIEM
solutions available in the market, namely ArcSight, QRadar, McAfee, LogRhythm, USM-
OSSIM, RSA, Splunk, and SolarWinds. This choice has been based on the performance and
trajectory of the companies developing this technology along the past decade.
In terms of behavioral analysis, and risk analysis and deployment, techniques and
tools for analyzing, evaluating, and guiding the optimal deployment of diverse security
mechanisms in the managed infrastructure (including multi-level risk-based metrics) must
be developed along with a framework for deploying diverse and redundant sensors.
Although most of the analyzed solutions provide user-friendly graphical interfaces,
visualization and reaction capabilities are limited to deal with huge numbers of collected
events. It is therefore important to develop visualization and analysis extensions, which
help give users a high-level of insight into the situation and a more efficient decision
making and reaction capability.
With regards to data storage and price, although most of the solutions analyzed
include good data storage capabilities, they are limited by the hardware availability and
they usually require additional products (and licenses based on data volume) with a
consequent increase in the price. Secure and elastic solutions based on cloud-of-clouds
storage for long-term SIEM data archival in diverse public clouds (e.g., Amazon S3, Amazon
Glacier, Windows Azure, Blob Store, etc.), are seen as promising alternatives with the ability
to customize policies to fit data retention needs.
Finally, the role of the SIEMs has also been studied in the near and long-term future
taking into account different aspects (e.g., political, economic, social, technological, en-
vironmental, and legal factors) in various critical infrastructures. From this analysis we
can conclude that conditions are good to foster investment in improving and extending
this technology as a key component not only for industrial control systems with security
operation centers, but also to provide cyber security management for SMEs with reduced
security knowledge and capacities.
Sensors 2021,21, 4759 22 of 28
Author Contributions:
G.G.-G. performed conceptualization, formal analysis, investigation, method-
ology, project administration, validation, visualization, writing original draft, and writing—review
and editing. S.G.-Z. performed conceptualization, formal analysis, investigation, methodology,
project administration, validation, visualization, and writing—original draft. R.D. performed concep-
tualization, funding acquisition, methodology, project administration, supervision, validation, and
writing—original draft. All authors have read and agreed to the published version of the manuscript.
Funding:
This research has been funded by the European Commission. It was started within the
context of the H2020 DiSIEM project (GA no. 700692) and has been completed as part of the H2020
STOP-IT project (GA no. 740610), CUREX project (GA no. 826404), and Cyber-MAR project (GA
no. 833389).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments:
The authors would like to thank Ignasio Robla, Alysson Bessani, Adriano
Serckumecka, Ana Respicio, Miruna. M. Mironescu, Frances Buontempo, Ilir Gashi, Ivo Rosa, and
the rest of partners from the DiSIEM project for their support, hard work, and collaboration.
Conflicts of Interest: The authors declare no conflict of interest.
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