IEEE Systems, Man and Cybernetics Magazine - April 2021 - 47

Table 3. The characteristics, benefits, and drawbacks of various
cybersecurity control strategies.
Method

Characteristics

Pros

Cons

Kalman filtering (e.g., KF, EKF,
and UKF)

Utilizes a state-space
mathematical model

Accurate results

Needs a precise model
Requires initial conditions

Observer based (e.g., high-gain
observers, Luenberger observers,
and LMI-based observers)

Use a state-space
-mathematical model

Accurate results

Requires a precise model

Sliding-mode observer

Utilizes a state-space
mathematical model

Convergence, robustness, simple
implementation, and disturbance
rejection

Chattering problem, -unmodeled
dynamic excitement, and
-energy loss

Intelligent (e.g., NNs and ANFIS)

Utilizes historical data

Excellent for modeling nonlinear
systems

Needs a large amount of
(- preferably labeled) data

Bayesian

Utilizes historical data

Efficiently trained using maximum
likelihood method

Requires sufficient cyberattack
data

SVM classifier

Uses historical data

Efficient for nonlinear classification

Needs sufficient cyberattack data

	

additional sensors in grids. Liu et al. [162] study the problem of BDD systems and introduce a cyberfusion methodology that includes an indicator known as abnormal
traffic-indexed SE (ATSE). ATSE combines information
network traffic flows with physical laws inherent to power
systems. It is used to mitigate the effects of malicious
activity on SE units. Applying this approach increases the
estimated residuals' magnitude on compromised devices,
leading to better detection in grids.
A multisensor prediction control approach is developed
in [163] to detect bad data. Phasor measurement units
(PMUs) are employed to control and estimate voltage magnitude to capture injection attacks. Simulations on an
IEEE 14-bus power system indicate promising results. A
KF-based cybersecure method is introduced in [164] for an

RTU

Power Networks
Control Unit

RTU

Attacker
Zi

Attacker

Attacker

SCADA
AGC
OPF
Operators

SE
EMS
〈

transmission line flows) and transmit them to automatic
generation control and SE units to approximate states
and control behaviors. The estimated states are also used
for energy management systems and optimal power
flows. The topology shown in Figure 10 is susceptible to
cyberattacks [150]. Adversaries may try to change control
commands, produce fake control actions, and manipulate
measurement data. Smart grids are subject to various
attacks, such as false data injections and DoS events
[151], [152]; their SE is analyzed to detect malicious activities [153], [154]. Smart grid cybersecurity methods relate
to power network vulnerability [155]-[157]. Measurements collected in smart grids might not be accurate due
to disturbances, malicious activities, and device failures.
Thus, any of these conditions will affect the SE precision.
There are traditional attack detection algorithms, known
as bad data detection (BDD) systems, in the literature
[158]-[160]. Methods rely on measurement redundancy
and can identify random outliers in measured data. They
can also identify simple attacks but may miss more
sophisticated ones.
Teixeira et al. [26] investigate a scenario where attackers have access to a smart grid. The attackers recognize a
partial model of the grid and organize a deception attack
that BDD methods cannot identify. The authors examine
two BDD approaches, including the performance index
test and the largest normalized test, to detect deception
attacks, showing that the methods fail. Furthermore, they
prove that the more system knowledge attackers have, the
more damage adversaries can inflict without being discovered. Similarly, a class of stealthy attacks, termed measurement data corruption, is studied in [161] on a smart
grid SE unit. The authors examine various deception
attack scenarios and demonstrate that organizing local
attacks without being detected is possible. Moreover, they
show that detection rates can be improved by employing

Zi

Figure 10. The power system structure and supervisory
control [149]. RTU: remote terminal unit; AGC: automatic
generation control; OPF: optimal power flow; EMS: energy
management system.

Ap ri l 2021

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE	

47



IEEE Systems, Man and Cybernetics Magazine - April 2021

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