IEEE Systems, Man and Cybernetics Magazine - April 2021 - 42

if 12 or fewer sensors are compromised, the state vector
converges in more than 90% of the cases. An observerbased H 1 output feedback controller is developed in [103]
to detect periodic DoS attacks in a discrete-time CPS. The
attacks are assumed to occur in control channels and sensor measurements. The cyclic dwell time switching method is applied to obtain an augmented form of the output
feedback controller and observer during the attacks. A
cyclic piecewise Lyapunov function is suggested to guarantee the closed-loop feedback system's stability. Figure 6
displays the state trajectories and controller signals under
a periodic DoS attack. Figure 6 shows that the state trajectories converge to zero.
A distributed observer-based approach is developed in
[104] to mitigate cyberattacks on CPS sensors and actuators. An observer-based control strategy using the distributed H 3 method is proposed, and the Riccati equation is
employed to solve the H 3 optimization problem. Reinforcement learning is trained offline, and a confidence
index is defined. The reinforcement learning algorithm
utilizes this index to determine the trustworthiness of
each neighbor. A low confidence index value indicates
that a node is under attack, and the CPS measurement

0.6

Cyclic Dwell Time
Switching Signals

0.5
State Responses

0.4

x 1(k )
Estimated x1(k )
x 2(t )
Estimated x2(t )

2
1

0.3
0.2

0

0.1

20
40
Time

0
-0.1
-0.2
-0.3
-0.4

0

10

20

30

40

50

Time
(a)
0.1
0

Control Signal

-0.1

u (k)

-0.2
-0.3
-0.4
-0.5
-0.6
-0.7
-0.8

0

10

20

30

40

50

Time
(b)
Figure 6. The (a) state responses and (b) controller

signal during a periodic DoS attack [103].

42	

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE Apri l 2021

data must be discarded. Simulations indicate that the
approach is resilient against cyberattacks. An observerbased attack detection method is introduced in [105]. An
unknown input observer (UIO) [106] is designed to detect
false data injection attacks using physical dynamics.
Detection is performed via the UIO residual. An adaptive
threshold is set for the residual to consider a disturbance's effect and enhance the attack detector's accuracy. Simulations on an IEEE 9-bus power system show
that the UIO is effective.
An event-triggered, observer-based control shame is
developed in [107] to detect DoS attacks in CPSs. First, a
decentralized event-triggered model is formulated using n
communication channels. It is assumed that the system
output information is transmitted to the channel by using
data generators with different samplers. DoS attacks are
likely to occur through the communications channels. A
state observer is designed, and a closed-loop system is
devised with linear state feedback. The closed-loop system's stability is proved using a linear matrix inequality
(LMI) method via a proper Lyapunov function. Simulations
on a spacecraft's flying system show that a tradeoff must
be made between attack intensity and transmission efficiency to keep the system stable. A multisensor resilient
attack detection method is proposed in [108]. It is assumed
that multiple redundant sensors are available and that
attackers can target them. High-gain observers are applied
to estimate states using each sensor measurement, and an
error correction mechanism is adapted to combine the
approximations. Simulations demonstrate that the scheme
is resilient against cyberattacks.
An observer-based resilient controller is proposed in
[109] for CPSs with communication channels. A reducedorder observer is developed to monitor the partial state of
various channels. Furthermore, an integrated approach is
constructed by adding a finite-time observer and switching scheme to resist attacks and disturbances. Simulations
indicate that the observer is efficient and can mitigate DoS
attacks. An observer-based resilient control method is
introduced in [110] for CPSs during DoS attacks. A switching adaptive SE method is designed using output measurements. Lyapunov theory is employed to prove stability
during DoS attacks through an LMI-based approach. Simulations on a power system indicate that all signals are
bounded in closed-loop systems and that errors converge
to a small area near the origin. A new abnormality detection method is introduced in [111] to identify attacks and
faults in multiagent systems. A group of cooperative detectors is developed to isolate abnormalities. Algorithm 2
illustrates the detector for multiagent CPSs.
Simulations using a group of grounded vehicles are conducted, and their results are compared with the |2 detector
via the KF-based method. Figure 7 describes the multiagent
vehicle system [111]. The proposed detection scheme can
isolate attacks and faults. Moreover, it improves the detector's accuracy in comparison with the |2 detector. After



IEEE Systems, Man and Cybernetics Magazine - April 2021

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