IEEE Systems, Man and Cybernetics Magazine - April 2021 - 45

approach across a sliding window is adapted to improve
estimation. Simulation tests on an IEEE 30-bus system
indicate that the strategy can accurately estimate true measurement values. A nonlinear autoregressive exogenous
model using an NN is constructed in [127] to detect deception attacks in CPSs. The model is trained through a wide
range of normal operating conditions. During attacks, measurement data diverge from their expected values. Therefore, the approach generates estimation errors, which are
used to detect attacks. Simulations on a dc microgrid show
that deception attacks are successfully isolated.
An intelligent secure control approach is developed in
[128] for nonlinear CPSs. Figure 9 displays the resilient
control structure using an NN attack estimator. A nonlinear NN estimator is developed based on a Gaussian radial basis function to estimate attacks, and a nonlinear
variable structure is designed for resilience. A Lyapunov
function is suggested to guarantee closed-loop system
stability. Another resilient, secure approach is proposed
in [129]. Long short-term memory using a recurrent NN is
developed to identify cyberattacks on measurements. An
ANFIS is employed to estimate true measurement values
by leveraging the physical coupling between actual and
compromised measurements. Simulations are conducted
with DoS and deception attacks. Results from an aircraft
auxiliary power unit indicate the method's effectiveness.
To detect DoS attacks in cellular networks, a deep
learning method using convolutional NNs is proposed in
[130]. Short message service flooding, signaling, and silent
calls are performed to trigger DoS attacks. A deep NN,
with 50 layers, is constructed to identify the strikes. The
CNN accuracy reaches 91% under normal and attack

conditions. A deep learning-based game-theoretic
approach is presented in [131] to set up various CPS attack
and defense strategies. The main advantage is that defenders and attackers can play the game and optimize their
actions, which is more likely to occur in real situations.
Different factors, such as the power of attackers and
defenders, attack costs, and budget allocations, are investigated. Simulations are performed on an IEEE 39-bus
power system. Attackers' and defenders' utilities are compared under strike conditions to analyze power losses during attacks and the amount of power saved through proper
defense strategies. The proposed approach helped transmission authorities efficiently protect power lines.
Naive Bayes Classifier
The naive Bayes classifier is a probabilistic classifier that
works based on Bayes's theorem using independent features. It is efficiently trained through the maximum likelihood approach [132]. Thus, it is a suitable candidate for
cyberattack detection and classification. Nguyen and
Cheng [123] introduce a data-driven method employing a
Table 1. The performance of different
- ptimization methods [125].
o
Method

Accuracy

SVM

74.9%

HNA-NN

83.2%

IWP-CSO with SVM

91.5%

IWP-CSO with HNA-NN

93.1%

Control Structure
Lyapunov Stability

NN Estimator
Attacker

Disturbance

References

Measurement
Adaptive Variable
Structure Control

Nonlinear CPS

Network

Control Signal
Error
Network

Attacker

Figure 9. The resilient control structure that uses an NN attack estimator [128].

	

Ap ri l 2021

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE	

45



IEEE Systems, Man and Cybernetics Magazine - April 2021

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