IEEE Systems, Man and Cybernetics Magazine - April 2021 - 46

combination of pairwise feature selection algorithms and a
naive Bayes classifier. Pairwise feature selection is utilized
to select optimal numbers of features. The naive Bayes
classifier is applied to distinguish between normal systems
and those that are under attack. The method is evaluated
on intrusion detection data sets [133], [134], and simulations
show that it performs well. A Bayesian-based attack detection approach is introduced in [135]. Joint attack detection
and SE are formed into a Bayesian framework based on the
hybrid Bernoulli random set (HBRS) method. Afterward, a
distributed approach is designed by integrating HBRS-
Bayesian results and results from Kullback-Leibler HBRS
averaging. Simulations are performed on a large-scale
power system, and the results prove the detector's effectiveness. An optimal Bayesian strategy is developed in [136]
to optimize protection costs against false data injection
attacks. Graph theory is utilized to identify power grid measurement sets. The Bayesian method is applied to estimate
measurement sets. Simulation is performed on an IEEE
14-bus power system, confirming the strategy.
SVM Classifiers
SVMs often build sets of hyperplanes in high-dimensional
space using system data sets, which can be harnessed for
cyberattack detection and classification. Therefore, SVMs
are highly efficient for nonlinear classification [137]. Huda
et al. [122] introduced semisupervised attack detection
and classification methods. The attack detection scheme
automatically combines information about unknown
attacks from an unlabeled data set combined with a
labeled one. Global k-means clustering and cosine similarity, as a distance measure, are applied to extract new
information and add it to the classification system. For
classification, the authors employ an SVM. The approach
is tested with a malware data set and compared with
naive Bayesian binary classification. The results indicate
that integrating the semisupervised method with the SVM
classifier yields 94% accuracy.
A comprehensive study of various classifiers for intrusion detection systems (IDSs) is performed in [138]. Various
information sources, including normal data, DoS attacks,
unauthorized access to local superusers (U2Sus), unauthorized access from a remote machine (R2L), and probing
Table 2. The accuracy (%) of various IDS
methods [138].
Class

SVM

RBP

SCG

OSS

MARS

LGP

Normal

98.42

99.57

99.57

99.64

99.71

99.64

Probing

98.57

92.71

85.57

92.71

56.57

99.86

DoS

99.11

97.47

72.01

91.76

99.6

99.9

U2Su

64

48

0

16

28

64

R2L

97.33

95.02

98.22

96.8

98.93

99.47

46	

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE Apri l 2021

(surveillance and other approaches), are investigated using
SVMs, resilient back propagation (RBP), the scaled conjugate gradient (SCG) algorithm, the one-step secant (OSS)
algorithm, multivariate adaptive regression splines
(MARSs), and linear genetic programs (LGPs), respectively.
Table 2 summarizes the accuracy of various IDS methods
[138]. Based on Table 2, LGPs produce the best results, and
SVM classifiers exceed RBP, SCG, and OSS methods in
terms of detecting probing, DoS, U2Su, and R2L attacks.
A two-stage attack detection approach is presented in
[139] for false data injection in CPSs. In the first stage,
principal component analysis (PCA) is applied to reduce
data dimensionality. Then, an SVM classifier is employed
to detect malicious activities. The strategy is implemented
on a three-bus power network, and the results demonstrate that the IDS has high accuracy. A multiagent supervised attack detection approach is developed in [140]. An
SVM algorithm with a decision tree mechanism is applied.
Attack detection is performed by each agent. Final decisions are made through consensus among all agents. Moreover, an adaptive rejection strategy is implemented to
tolerate DoS attacks. Simulations on an IEEE 39-bus
power system indicate that the method efficiently detects
attacks and is resilient against DoS events. The characteristics, benefits, and drawbacks of various cybersecure
control methods, inducing model-based and data-driven
approaches, are summarized in Table 3.
Prominent Application Domains
CPSs are critical infrastructures that play an essential
role in growing economies and industries around the
world. In the following, research work focusing on cybersecurity in some of the most important application
domains, including smart grids, water networks, process
control systems, automated vehicles, and medical systems, is reviewed.
Smart Grids
Smart grids are some of the most common CPSs. They
integrate power systems with cybersystems that enable
components to communicate in an embedded intelligent
network. Therefore, they combine advanced control technology with communication [141]. In a smart grid, sensors
are outfitted to measure information, such as current and
voltage fluctuations, and transmit data to other nodes.
Cooperation between physical components and communication systems enhances grid performance and leads to
better power system efficiency. However, such systems are
prime targets for cyberattacks [142]-[146].
Smart grids are supervised by SCADA [147], [148]. Figure 10 displays a power system str ucture a nd its
supervisory control [149]. These networks are normally
distributed across wide geographical areas and have
many buses [149]. In such large-scale systems, the
SCADA utilizes remote terminal units to observe power
data (e.g., bus voltages, bus power injections, and



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