









































Pa
ge

 
1



Pa
ge

 
1

American Journal of  Smart 
Technology and Solutions (AJSTS)

New Obstacles to Smart City Cybersecurity
Abdullah Alsaeed1*

Volume 1 Issue 1, Year 2022
https://journals.e-palli.com/home/index.php/ajsts

Article Information ABSTRACT

Received: October 06, 2022

Accepted: October 29, 2022

Published: November 03, 2022

This article provides a concise description of  the criteria that may be evaluated to deter-
mine the adoption of  smart grid approaches that improve cybersecurity. It is necessary, 
from a functional point of  view, to establish the degree to which cyber resilience may be 
increased by implementing solutions that are efficient in terms of  cost. The problem of  
cybersecurity for smart grids has been the focus of  several research and initiatives. In this 
study, the detection and diagnosis of  False Data Injection (FDI) attacks are investigated in 
detail concerning their accuracy, processing time, and resilience to outside influences. No 
one method can be applied to all power systems. Therefore, a comparison and statistical 
analysis of  the newly reported approaches for detecting and recognizing cyberattacks are 
conducted here.

Keywords
Cyber-attack, Cybersecurity, 
False Data Injection (FDI) , 
Resilience, Smart City, 
Smart Grid

1 Department of  Computer Science, University of  Manchester, Saudi Arabia
* Corresponding author’s e-mail: alsaeed.866@gmail.com

INTRODUCTION
One of  the many advantages of  the current power city’s 
technological design, which is often referred to as the 
“smart city” in certain circles, is that it allows for more 
efficient integration of  renewable energy sources (RESs). 
However, due to the vast quantity of  data that has to 
be sent for the system to function correctly, the smart 
city relies on an improved communication infrastructure 
to function properly (Aoufi et al., 2020; Mohammadi et 
al., 2019b; Mohammadi & Neagoe, 2020). As a result, 
cyber-attacks on smart city have increased. Invasions 
of  a company’s communication networks may drive up 
operating expenses dramatically (Nikmehr & Moghadam, 
2019) or could impede the efficient functioning of  the 
system (Q. Wang et al 2019). For example, cyber-attacks 
on Ukraine’s power infrastructure in 2015 caused several 
hours of  extensive power outages (Aoufi et al., 2020). 
Smart city operators must immediately identify, detect, 
and respond to such assaults to guarantee the system’s 
integrity and correct functioning. This procedure 
is referred regarded as having cyber resilience as its 
defining characteristic. Correctly identifying cyberattacks 
is reportedly the first step in bolstering resilience 
throughout the attack and post-attack phases. This is the 
opinion of  those who operate power systems. This is 
because the strike cannot be predicted with any degree 
of  accuracy. As a consequence of  this, several research 
activities have been carried out over the last decade to 
effectively detect and identify intrusions as a component 
of  the cyber-resistance of  the smart city (Biggio & Roli, 
2018; Otuoze et al., 2018; Sharafeev et al., 2018; Q. Wang, 
W. Tai, Y. Tang, & M. Ni, 2019; Wang & Lu, 2013).

LITERATURE REVIEW
Numerous research projects have been conducted 

to verify the precision of  cyberattack identification 
and detection, help accelerate processing, and boost 
resistance from external influence. Malicious meters 
might be detected more precisely using an AI-based 
approach, as described by (Khanna et al., 2018). Using 
machine learning, a strategy was published by (D. Wang 
et al., 2019) that might better recognize different types of  
power city disruptions and cyber-attacks. Reinforcement 
Learning (RL) was proposed to cope with diverse Partially 
Observable Markov Decision Process POMDPs (Kurt et 
al., 2018). 
Training the defender with low-magnitude assaults and 
decreasing an attacker’s attacking space increased the 
strategy’s robustness (Kurt et al., 2018). A multivariate 
Gaussian-based model was described for power 
distribution system cyber-attack detection (An & Liu, 
2019). The isolation forest technique was introduced 
by (Ahmed et al., 2019) to detect hidden data integrity 
breaches in the smart city, which is an unsupervised 
approach. To further speed up the detection of  the 
assault, the suggested solution used minimal complexity. 
A randomized trees-based machine learning approach 
was presented to identify stealthy cyberattacks in the 
smart city successfully (Acosta et al., 2020). In addition, 
the recommended approach was faster to compute and 
more resilient to noisy input than previous machine 
learning-based attack detection techniques. 
The distinction between data manipulation 
alterations and physical city adjustments was made by 
(Mohammadpourfard et al., 2020), enabling the attack 
detection system to work successfully even when ideas 
moved. The unsupervised False Data Injection (FDI) 
attack technique (Mohammadpourfard et al., 2017) 
effectively-recognized attacks under various scenarios. It 
showed resistance to the integration and reconfiguration 

https://journals.e-palli.com/home/index.php/ajsts
mailto:alsaeed.866%40gmail.com?subject=


Pa
ge

 
2

https://journals.e-palli.com/home/index.php/ajsts

Am. J. Smart. Technol. Solutions 1(1) 1-8, 2022

of  renewable energy sources (RESs) into power networks. 
It was discovered by (Moslemi et al., 2017) that one of  the 
most effective ways to identify assaults on the smart city 
is to use the Maximum Likelihood (ML) estimate. 
In addition, the suggested strategy reduced the computing 
load by minimizing the complexity of  the ML estimation 
issue. (Li et al., 2018) looked into an exact and quick 
approach in computing for detecting FDI assaults on 
smart city. Noise-free data was not a problem for the 
proposed method. As reported by (Hao et al., 2016), the 
Markov Decision Process (MDP) technique was used to 
identify and evaluate the susceptibility of  power city to 
cyberattacks in a dynamic setting. (Zhao et al., 2018) They 
have shown their method’s robustness for detecting FDI 
assaults in noisy environments.
The resilience of  the smart city and its ability to withstand 
cyberattacks take up a substantial portion of  the attention 
of  this essay. The most common types of  cyberattacks, 
known as FDI assaults, are discussed in this section. The 
most current research publications to be published are 
compared to one another and are expounded.
The components of  the paper are detailed below. Second, 
the principles of  cyber-attack detection and identification 
in the smart city are discussed in Section 2, while Section 
3 focuses on current quantitative methodologies for 
cyber-attack detection and identification. This is followed 
by Section 4, which concludes the paper. Finally, in the 
fourth part, we will evaluate several strategies based on 
their resilience.

The Smart City Cyber-Attack Detection and 
Identification Fundamentals
As one of  the new cyber-physical systems, the smart 
city is built on the physical power infrastructure, which 
includes power generation, distribution, and consumption 
systems, and the dense integration of  communication 
infrastructure with specialized hierarchical control 
structures (Mohammadi et al., 2019c; Ostadijafari et 
al., 2019). The physical power infrastructure, which 
consists of  power generation, consumption systems, 
distribution, and the dense integration of  communication 
infrastructure with specialized hierarchical control 
structures, forms the foundation of  the smart city, as 
one of  the new cyber-physical systems. Communication 
systems usually comprise actuation devices and smart 
sensors at the local control level. However, these 
systems also incorporate smart controllers, smart meters, 
automation units, Phase Measurement Units (PMUs), and 
distributed generations at the higher control levels, such 
as cyber layers (Mohammadi et al., 2019a). 
They are susceptible to cyberattacks due to the smart city’s 
extensive penetration of  communication infrastructure, 
which has led to the proliferation of  connected devices. 
Most threats to the systems that distribute power 
originate from unfriendly outsiders, malicious insiders, 
non-malicious insiders, and mother nature herself. 
Most of  these dangers originate from smart homes and 
businesses outfitted with smart meters. Malicious agents 

are a risk because they can infect everyone with malware 
and viruses or zero in on specific computer systems to 
break into them, disrupt their operation, or cause damage 
to them.
The most prevalent types of  cyber-attacks in the smart 
city are Denial of  Service (DoS) and FDI (Nguyen et al., 
2020). Most denial-of-service attacks are geared toward 
interrupting the data transfer process by focusing their 
attention on the communication infrastructure. It’s 
possible that scam data streams could be continuously 
flooded into the network or that synchronized data 
flooding would be used to target control signals (Nguyen 
et al., 2020). In contrast to DoS assaults, FDI assaults 
often take the form of  data packet manipulation, which 
may occur at varying degrees of  severity (Nguyen et al., 
2020). 
In addition, FDI attacks may be designed to target several 
data packets included inside communication protocols 
(Nguyen et al., 2020). These data packets can consist 
of  sensor/actuator software calibrations and protective 
relays, feedback signals and commands. As a result, the 
smart city might experience poor performance, instability, 
and even blackouts due to attacks by FDI (Liu et al., 2019).
It is necessary to quickly and accurately detect and identify 
any hostile cyber-attacks to improve the cybersecurity 
of  smart city. As well as reducing computation cost and 
complexity, defensive measures must be implemented 
to strengthen or restore the system’s resistance against 
cyberattacks. The inability to differentiate between regular 
system interruptions and dynamics, such as changes 
in command signals and cyberattacks, connection/
disconnection of  power generating units, load switching, 
are barriers to detecting cyberattacks. Regular system 
interruptions and dynamics include these things. 
Enhanced control mechanisms and careful evaluation of  
the system model’s nonlinear character are necessary to 
deal with the nonlinearities, uncertainties, and disruptions 
inherent in the system.
One way of  determining deviations and abnormalities 
is to estimate the system states under normal operating 
conditions and then compare those estimates to the 
actual system states. The steady-state states of  the system 
were calculated using a Weighted Least Square (WLS) 
estimator, and the results are shown in (Xu et al., 2017). 
In (Sreenath et al., 2017), an attempt was made to solve 
the problem of  WLS’s inability to converge on a solution. 
This led to the development of  recursive WLS. 
Dynamic estimating techniques are required to do a 
quick study on power systems. These methods must 
take into consideration the system’s initial states. The 
Kalman Filtering (KF) method is widely used non-static 
estimation approach that incorporates a corrective term 
to reduce the number of  errors caused by state estimation 
(Manandhar et al., 2014). Extended Kalman Filtering 
(EKF), which considers the system’s nonlinearities, was 
examined by (Abbaspour et al., 2019; Chakhchoukh et 
al., 2019) to detect FDI assaults. In all of  these model-
dependent detection strategies, inaccuracies in the models 

https://journals.e-palli.com/home/index.php/ajsts


Pa
ge

 
3

https://journals.e-palli.com/home/index.php/ajsts

Am. J. Smart. Technol. Solutions 1(1) 1-8, 2022

assaults, the cumulative error rate for state variables 
was just 1%. In order to get more precise results for 
calculating the tampering meters, an extra load estimator 
has been included in the current model.
Neural Networks (NN) and Artificial Neural Networks 
(ANN) with a single hidden layer and forward connections 
were proposed to achieve an appropriate learning rate. 
Because these networks were trained using historical data, 
the suggested estimator could identify an FDI assault, even 
if  it came from a small number of  compromised meters. 
This was made possible because these networks were used 
to train the estimator. In the event of  a widespread FDI 
attack, our technique ensures the correct identification 
of  both the attack and the tempered meters. (D. Wang et 
al., 2019) outlines a supervised learning method that may 
detect cyberattacks on the smart city using previous data 
and log information. With an accuracy of  93.9 percent 
and a detection rate of  93.6 percent, the presented 
strategy is superior to other previously proposed 
techniques, such as the Random Forest (RF) method, 
and the K-Nearest Neighbors (KNN) algorithm. Both of  
these techniques have a detection rate of  93.6 percent. In 
(Kurt et al., 2018), the RL approach was used for the first 
time to detect internet assaults on the smart city. This 
form of  cyberattack was categorized as a POMDP since 
the attacker could compromise the legitimate states of  
the smart city, which the system operator would not have 
been able to tell was compromised in the first place. The 
solution shown by (Kurt et al., 2018) does not involve 
using a model, as was mentioned, and it also took much 
less time to accomplish. A person who acts alone.
 The RL technique, which was presented from the 
perspective of  a system defender, proved effective in 
detecting low-magnitude assaults. As a consequence, the 
defense may observe tiny variations in the states of  the 
smart city independent of  the method the attacker is using. 
The model-free strategy suggested showed evidence of  
resistance to the unknown system states. FDI assaults 
in the cyber-physical system of  a power distribution 
city were detected using a multivariate Gaussian-based 
technique (An & Liu, 2019). This method was applied. 
It is possible to differentiate between transient and 
persistent assaults by examining the measurement data 
produced by micro-PMUs. 
Power distribution systems are divided into zones with 
comparable voltage profiles using the K-Means clustering 
technique. In order to account for this shift, fewer 
micro-power management units (PMUs) were used. The 
accuracy and precision that were shown were sufficient 
for the identification of  transient assaults. However, the 
technique suggested to be used in continuous assaults 
was based on the imprecision of  prediction, which the 
circumstances of  a smart city may influence. 
Enhancing regression models may lower the proportion 
of  erroneous predictions and boost attack detection 
accuracy. According to the information in reference 14, 
the smart city is now targeted by a covert data integrity 
attack. A technique for feature extraction based on 

progressively degrade their effectiveness and potentially 
result in false positives.
The actual execution of  the solution becomes more 
challenging when recursive techniques and exact models 
are used since they increase the bar for the amount of  
computing power required to estimate the system’s state 
accurately. Data-driven solutions have been created so 
that the difficulties connected with model-dependent 
detection approaches may be addressed. In general, 
the data-driven methodology may be classified into 
one of  three groups: supervised learning techniques, 
unsupervised learning techniques, and semi-supervised 
learning techniques. Each input is mapped to its one-of-
a-kind output in the algorithms that use labels derived 
from the labeled dataset.
It is usual practice to use supervised learning techniques 
where various ranges of  cyber-attack simulations may be 
produced to obtain the necessary training dataset (Ayad 
et al., 2018; Fenza et al., 2019). As opposed to that, it is 
feasible to identify a meaningful pattern in unlabeled 
data by using algorithms that do not need supervision. 
However, using such approaches to identify cyberattacks 
is far less common than supervised algorithms. In 
addition, you should only utilize them when cyberattacks 
are not found in any of  the obtained datasets (Zanetti et 
al., 2017). Therefore, acquiring the same training datasets 
under various operating settings is vital, are valid for 
supervised and unsupervised learning strategies.
 Since the efficiency of  detection algorithms is wholly 
dependent on the datasets they collect, there is a high risk 
that these algorithms may become overfit. Consequently, 
the system has difficulty recognizing instances of  
cyberattacks that were not included in the training data 
set. A data-driven and model-based detection technique 
was studied in the paper (Sargolzaei et al., 2019) as a 
potential solution to the problem that had been found 
before. In addition, strategies based on semi-supervised 
learning may be used when a trial-and-error method must 
be utilized to rectify or change the following control 
action following the input from the control actions that 
came before (Chen et al., 2018). 
The main challenges in the way of  the development of  
cybersecurity for the smart city are the detection accuracy, 
the processing complexity, and the resistance to external 
influences. These challenges apply to both data-driven 
and model-based detection systems.

Methods for the Detection and Identification of  
Cyber Attacks
Data-Driven Procedures
To effectively identify cyberattacks on the smart city, data-
driven technologies, such as machine learning techniques, 
have seen widespread application in recent years 
(Apruzzese et al., 2019).In (Khanna et al., 2018), it was 
suggested to use a supervised AI-based load estimator 
to compare anticipated loads with actual meter data to 
identify which meters are vulnerable to FDI assaults. As 
a direct result of  the model’s capability to identify FDI 

https://journals.e-palli.com/home/index.php/ajsts


Pa
ge

 
4

https://journals.e-palli.com/home/index.php/ajsts

Am. J. Smart. Technol. Solutions 1(1) 1-8, 2022

principal component analysis (PCA) was used to make 
the issue more manageable, and high-dimensional data 
was transformed into low-dimensional space. The 
“isolation forest” method, which may detect irregularities 
in state estimation measurement characteristics, is based 
on unsupervised machine learning. It exhibited a better 
accuracy rate when comparing the suggested method to 
more conventional machine learning-based tactics.
A shorter processing time was needed to detect 
cyberattacks due to the technique’s lower computational 
complexity. In (Acosta et al., 2020), the smart city’s state 
estimation-measurement capabilities were used to detect 
stealthy cyberattacks by applying supervised learning.
Large-scale power systems have a high dimensional space. 
Hence it was chosen to use a Kernel Principal Component 
Analysis (KPCA) approach to reduce the complexity 
of  the problem occurring in the system and accurately 
represent the information in a lower-dimensional space. 
Furthermore, the properties of  KPCA were used to 
demonstrate that the proposed method is reliable in 
the presence of  imbalanced datasets. It was also able to 
detect cyberattacks on the smart city with a high degree 
of  accuracy, despite taking less computer time than other 
machine learning-based methods, such as classic PCA. 
(Mohammadpourfard et al., 2020) conducted research 
on the cybersecurity of  smart city to evaluate the effect 
of  concept drift, also known as the detection of  physical 
changes that are the consequence of  variations in smart 
city data manipulation. The efficiency of  this technique 
in spotting malicious cyber activity was subjected to 
extensive testing and analysis. Another study presented 
(Mohammadpourfard et al., 2017) a technique for detecting 
cyberattacks that consider the system’s reconfiguration 
and incorporation of  RES. 
The F-Test was applied to distinguish between the 
regular and attacked state vectors. It was found that more 
investigation is necessary for the suspect samples that 
defy the F-premise testing. As a result, the comparison 
index utilized to evaluate failed samples was the difference 
between suspected vectors and the average from 
comparable system state vectors. This was accomplished 
with the assistance of  three outlier detection algorithms, 
including, Interquartile Range (IQR), Median Absolute 
Deviation (MAD), and Fuzzy C-Means (FCM) clustering, 
methods. According to the results, the proposed method 
could detect FDI assaults with a high degree of  accuracy 
while unaffected by changes in the parameters.

Estimation Methods for the State
Correct state estimates may assist in keeping the smart 
city safe and fully controlled (Yong et al., 2016). On the 
other hand, state estimators are open to assault by FDI. 
Such attacks may defeat Bad Data Detection (BDD) 
techniques and modify the state estimate.(Moslemi et al., 
2017) presented an example of  a decentralized method 
for discovering smart city attacks based on ML estimates. 
ML estimate was used to identify attacks since it could be 
transformed into a chordal embedding space. With the 

help of  the Kron reduction of  the Markov network of  
phase angles, the approach that was provided was able 
to segment the ML estimation problem into a number 
of  distinct local ML estimation problems. The suggested 
method is decentralized, which provides utilities with 
more anonymity. By minimizing the size of  the problem, 
the quantity of  labor required to solve it is also reduced. 
Furthermore, due to the properties of  the attack matrix, 
which is sparsely, and the measurement matrix, which 
has a low rank, the FDI attack detection problem may be 
reframed as a matrix separation problem (Li et al., 2018). 
This is possible because of  the similarities between the 
two matrices. 
The modern methods for separating matrices, such as 
the the Double-Noise-Dual-Problem (DNDP)-ALM, 
Augmented Lagrangian Method (ALM), the Low-Rank 
Matrix Factorization (LRMF), and, suffer from the 
increase in the amount of  time needed for computing 
and a reduction in the amount of  accuracy achieved. 
To successfully solve this issue, a strategy named Go-
Decomposition was studied. The suggested approach 
displayed adequate accuracy in separating FDI attacks 
compared to the LRMF method and approximately 
similar accuracy compared to the ALM and DNDP-ALM 
methods when the environment was free of  background 
noise. Furthermore, the solution offered to the issue had 
a fair calculation time and could protect the smart city 
against assaults on a broad scale.
An MDP that was designed to simulate the attack strategy 
of  the attackers was published by (Hao et al., 2016). 
Research on the knowledge and scenarios linked to the 
smart city was carried out in two phases. First, in an MDP 
with a short time horizon, it is conceivable for adversaries 
to determine the current state of  the intelligent city over 
a relatively short amount of  time. After investigating the 
probabilities of  an assault, the most effective method from 
the viewpoint of  the aggressor was found. According to 
the findings of  the vulnerability analysis, the suggested 
method is resilient against the parametric uncertainties 
present in an MDP situation and the operators’ dispatch 
strategy. An operator-perspective technique for assessing 
the susceptibility of  nonlinear state estimators to FDI 
assaults is presented in reference (Zhao et al., 2018).
A reliable approach for recognizing FDI assaults was 
developed, including using a subset of  safe PMU 
measurements to investigate the measurement’s statistical 
consistency. 
These security approaches, unaffected by abnormalities 
and render the system completely transparent, may be 
used to determine whether or not an FDI attack has 
occurred. A dependable Huber M-estimator was also 
used to accomplish accurate FDI assault detection. 
The suggested approach was unaffected by secure 
measurements and insufficient and noisy data. It was 
stated by (Deng et al., 2018) that FDI attacks might be 
carried out against power distribution networks since the 
statuses of  these networks could be expected based on 
the data on power flow. 

https://journals.e-palli.com/home/index.php/ajsts


Pa
ge

 
5

https://journals.e-palli.com/home/index.php/ajsts

Am. J. Smart. Technol. Solutions 1(1) 1-8, 2022

According to the simulation’s findings, the FDI attack 
can be carried out without being discovered by the BDD 
approaches if  the attacker correctly anticipates one state 
of  the system. FDI attacks on the electrical city have been 
investigated (Margossian et al., 2019), operating on the 
assumption that the attackers had some understanding of  
the system. After that, the demonstration FDI attack on 
the partial city was carried out to show how undetected 
FDI attacks may be. A strategy based on state estimates 
was later developed to protect power city against assaults 
carried out in the name of  foreign direct investment 
(FDI) that go unreported. The Basic Measurement Set is 
a mechanism that was developed by (Sreeram & Krishna, 
2019) to protect the smart city against FDI assaults by 
securing n-1 meter in n-bus power systems. This solution 
was given the moniker “the Basic Measurement Set” 
(BMS). The approach was then altered to determine 
a subset of  the optimum BMS to reduce the level of  
vulnerability shown by the system if  less than n - 1 meter 
could be safeguarded.

Various Other Approaches
As was said before, the primary objective of  cyberattacks 
is to influence the condition that is expected to be present 
in the smart city. Therefore, in addition to the data-driven 
methodology and the state estimation technique, other 
methodologies, such as Game Theory, have been utilized 
to analyze the vulnerability of  the smart city to the 
possibility of  cyberattacks (Apruzzese et al., 2019; Biggio 
& Roli, 2018).
In (Q. Wang, W. Tai, Y. Tang, M. Ni, et al., 2019), the 
features of  FDI assaults, as seen from the attacker’s 
viewpoint, were described to reveal the vulnerabilities 
present in current BDD approaches. After that, a two-
layer defensive paradigm that included detection and 
protection strategies was presented from the defender’s 
point of  view. A zero-sum, static Game Theory was used 
to identify the most effective defensive and attacking 
tactics. It was found that the minimax-regret technique 
could be used to design a cost-effective defense that could 
be used against an assault that used load redistribution 
(Abusorrah et al., 2017).
There was an effort made to spread the load, and the 
algorithm’s goal was to reduce the amount of  economic 
damage caused. Because the protective strategy of  
the smart city is susceptible to time-varying loading 
situations, it is necessary to develop an algorithm that can 
account for these fluctuations. A Game-Theoretic model 
was developed and tested to meet this need under various 

loading scenarios. Subsequently, a multi-level insolvable 
problem was transformed into a bi-level solvable 
optimization issue. A greedy implicit enumeration method 
was also utilized to identify the optimal global solution. 
In (Pilz et al., 2020), an investigation was conducted into 
how the influence of  FDI assaults on compromising 
anticipated demand data.
A model based on Game Theory was devised to assist 
utilities in awarding against these kinds of  assaults, and 
the Nash equilibrium was uncovered. The best kind 
of  monitoring for low-impact cyberattacks is none; 
nevertheless, for all other types of  assaults, a range of  
defense techniques should be devised. Researchers from 
(Hasan et al., 2020) looked at a cyberattack game in 
which the attacker and the defense were fighting against 
one another to see who would emerge victoriously. The 
attacker chose and targeted critical power substations to 
do the most damage possible to the system while staying 
within the allotted spending limit.
The vast majority of  important power substations were 
taken simultaneously to reduce the amount of  damage 
done to the system from the standpoint of  the defense. 
We used polynomial-time algorithms to determine the 
worst possible dynamic assault that could be launched 
and the most effective defensive plan. The strategy given 
was more effective, less challenging, and capable of  
attacking a wider range of  target systems than the best 
practices already in place. It was also more efficient and 
less complex than those practices.
In (Gao & Shi, 2020), a method based on dynamic 
game theory was presented to determine the level 
of  vulnerability posed by cyber-physical systems. 
Furthermore, a mathematical programming model 
consisting of  three groups a defender, an attacker, and 
a defender was investigated in the context of  a system 
recovery delay and a distributed denial-of-service attack. 
To overcome the challenge of  optimization, a cutting-
edge technique known as Particle Swarm Optimization 
(PSO) was used. The developed strategy proved to be 
quite successful when it came to finding susceptible 
transmission lines in power networks.

Detection and identification of  cyberattacks; A 
comparison 
The many methods of  detecting and identifying cyber-
attacks discussed in this research are compared in Table 1. 
For this comparison, we will use our resilience criteria for 
accuracy, computational load, and resistance to external 
variables.

Table 1: Methods of  detecting and identifying cyber-attacks
Author Objective Method Proposed Criteria For Adaptability and 

Resurgence
Accuracy Complexity External 

robustness
(Gao & Shi, 2020) detection and analysis 

of  cyber-attacks and 
vulnerabilities

Dynamic Game 
Theory

✓

https://journals.e-palli.com/home/index.php/ajsts


Pa
ge

 
6

https://journals.e-palli.com/home/index.php/ajsts

Am. J. Smart. Technol. Solutions 1(1) 1-8, 2022

(Hasan et al., 2020) detection of  cyber attacks Game Theory ✓

(Pilz et al., 2020) detection and identification 
of cyber attacks.

Game Theory ✓ ✓ ✓

(Abusorrah et al., 
2017)

detection of  
cyber-attacks

Game Theory based 
on the Minimax-
Regret Method

✓

(Q. Wang, W. Tai, Y. 
Tang, M. Ni, et al., 
2019)

Detection and 
identification of  
cyber-attacks.

Zero-sum Static 
Game Theory

✓ ✓

(Sreeram & Krishna, 
2019)

detection and analysis 
of  cyber-attacks and 
vulnerabilities

State Estimation ✓ ✓ ✓

(Margossian et al., 
2019)

detection and analysis 
of  cyber-attacks and 
vulnerabilities

State Estimation 
Based on power flow 
analysis

✓ ✓

(Deng et al., 2018) detection and analysis 
of  cyber-attacks and 
vulnerabilities

State Estimation ✓ ✓

(Zhao et al., 2018) Vulnerability and 
intrusion detection

Huber M-Estimator ✓ ✓

(Hao et al., 2016) Vulnerability and 
intrusion detection

Markov Decision 
Process-Based Method

✓

(Li et al., 2018) Detection of  Internet-
based cyberattacks

Go-Decomposition 
Algorithm

✓ ✓

(Moslemi et al., 2017) detection of  
cyber-attacks

Gaussian Markov 
Random Field Method

✓ ✓ ✓

(Mohammadpourfard 
et al., 2017)

detection and identification 
of cyber-attacks.

Unsupervised 
Learning Algorithm

✓ ✓

(Mohammadpourfard 
et al., 2020)

detection of  
cyber-attacks

Isolation Forest 
Method

✓ ✓

(Acosta et al., 2020) detection of  cyber attacks KPCA-Based Method ✓

(Acosta et al., 2020) detection of  
cyber attacks

Isolation Forest PCA-
Based Method

✓

(An & Liu, 2019) detection of  
cyber attacks

Multivariate 
Gaussian-Based 
Method

✓ ✓ ✓

(Kurt et al., 2018) Detection of  Internet-
based cyberattacks

Reinforcement 
Learning-Based 
Algorithm

✓ ✓

(D. Wang et al., 2019) Detection and 
identification of  
cyber-attacks.

Supervised Learning 
Algorithm

✓ ✓

(Khanna et al., 2018) Identification of  
Malicious Meters

AI-Based Algorithm ✓

Recent research, as seen in this table, has emphasized 
precision as a primary priority. However, accuracy remains 
a significant barrier to adopting data-driven solutions in 
the energy industry. In most cases, the computational 
burden may be lowered by using more potent processors 
and computing methods, such as parallel or distributed 
computing, which incur costs. In addition, ensuring safe 
operations may incur an unwanted but unavoidable cost 
due to the computational burden.

There is also a great deal of  literature about resilience. In 
several recent studies, the degree to which the smart city’s 
security is enhanced remains unclear. According to Table 
1 of  the operational aims, detecting and identifying online 
cyberattacks should be the most significant. Due to the 
unexpected behavior of  renewable energy sources, energy 
management systems are plagued by high uncertainty and 
stochasticity.
Smart meters and PMUs with IoT capabilities might 

https://journals.e-palli.com/home/index.php/ajsts


Pa
ge

 
7

https://journals.e-palli.com/home/index.php/ajsts

Am. J. Smart. Technol. Solutions 1(1) 1-8, 2022

provide fraudsters with various attack surfaces. Finally, 
controlling and regulating smart city is made more 
difficult by the need for quick detection and diagnosis 
of  cyberattacks. The application of  AI models in dealing 
with small datasets for training and testing, as well as the 
complex behavior of  attacker and defender models, is 
demonstrated by the fact that game theory and RL models 
fit all three criteria. These cutting-edge technologies for 
cyberattack detection in power city may be helpful in the 
future.

CONCLUSION
At the beginning of  this article, we discussed improvements 
to the smart city’s overall level of  cybersecurity. Recent 
academic research has focused on investigating ways to 
defend the smart city from intrusions by digital hackers. 
The accuracy, computing complexity, and resistance 
to external influences of  FDI attack detection and 
identification have been the focus of  further research 
in this study. In addition, this study has looked at the 
resilience of  FDI assaults. All of  the criteria mentioned 
in this study can be quantified, enabling operators of  the 
system to assess the degree to which the implementation 
of  practical financial solutions may improve the system’s 
resilience.

ACKNOWLEDGEMENT
The author is thankful to the University of  Manchester, 
Saudi Arabia, for the continuous support of  this research 
study. 

Funding
No funding sources are reported.

Conflict of  interest
The author does not have any conflict of  interest. 

REFERENCES
Abbaspour, A., Sargolzaei, A., Forouzannezhad, P., 

Yen, K. K., & Sarwat, A. I. (2019). Resilient control 
design for load frequency control system under false 
data injection attacks. IEEE Transactions on Industrial 
Electronics, 67(9), 7951-7962. 

Abusorrah, A., Alabdulwahab, A., Li, Z., & Shahidehpour, 
M. (2017). Minimax-regret robust defensive strategy 
against false data injection attacks. IEEE Transactions 
on Smart Grid, 10(2), 2068-2079. 

Acosta, M. R. C., Ahmed, S., Garcia, C. E., & Koo, I. 
(2020). Extremely randomized trees-based scheme for 
stealthy cyber-attack detection in smart grid networks. 
IEEE Access, 8, 19921-19933. 

Ahmed, S., Lee, Y., Hyun, S.-H., & Koo, I. (2019). 
Unsupervised machine learning-based detection of  
covert data integrity assault in smart grid networks 
utilizing isolation forest. IEEE Transactions on 
Information Forensics and Security, 14(10), 2765-2777. 

An, Y., & Liu, D. (2019). Multivariate Gaussian-based 
false data detection against cyber-attacks. IEEE 

Access, 7, 119804-119812. 
Aoufi, S., Derhab, A., & Guerroumi, M. (2020). Survey 

of  false data injection in smart power grid: Attacks, 
countermeasures and challenges. Journal of  Information 
Security and Applications, 54, 102518. 

Apruzzese, G., Colajanni, M., Ferretti, L., & Marchetti, 
M. (2019). Addressing adversarial attacks against 
security systems based on machine learning. 2019 11th 
international conference on cyber conflict (CyCon).

Ayad, A., Farag, H. E., Youssef, A., & El-Saadany, E. F. 
(2018). Detection of  false data injection attacks in 
smart grids using recurrent neural networks. 2018 
IEEE Power & Energy Society Innovative Smart Grid 
Technologies Conference (ISGT). 

Biggio, B., & Roli, F. (2018). Wild patterns: Ten years 
after the rise of  adversarial machine learning. Pattern 
Recognition, 84, 317-331. 

Chakhchoukh, Y., Lei, H., & Johnson, B. K. (2019). 
Diagnosis of  outliers and cyber attacks in dynamic 
PMU-based power state estimation. IEEE Transactions 
on Power Systems, 35(2), 1188-1197. 

Chen, Y., Huang, S., Liu, F., Wang, Z., & Sun, X. (2018). 
Evaluation of  reinforcement learning-based false data 
injection attack to automatic voltage control. IEEE 
Transactions on Smart Grid, 10(2), 2158-2169. 

Deng, R., Zhuang, P., & Liang, H. (2018). False data 
injection attacks against state estimation in power 
distribution systems. IEEE Transactions on Smart Grid, 
10(3), 2871-2881. 

Fenza, G., Gallo, M., & Loia, V. (2019). Drift-aware 
methodology for anomaly detection in smart grid. 
IEEE Access, 7, 9645-9657. 

Gao, B., & Shi, L. (2020). Modeling an attack-mitigation 
dynamic game-theoretic scheme for security 
vulnerability analysis in a cyber-physical power 
system. IEEE Access, 8, 30322-30331. 

Hao, Y., Wang, M., & Chow, J. H. (2016). Likelihood 
analysis of  cyber data attacks to power systems with 
Markov decision processes. IEEE Transactions on 
Smart Grid, 9(4), 3191-3202. 

Hasan, S., Dubey, A., Karsai, G., & Koutsoukos, X. 
(2020). A game-theoretic approach for power systems 
defense against dynamic cyber-attacks. International 
Journal of  Electrical Power & Energy Systems, 115, 105432. 

Khanna, K., Panigrahi, B. K., & Joshi, A. (2018). AI-based 
approach<? show [AQ=”” ID=” Q1]”?> to identify 
compromised meters in data integrity attacks on 
smart grid. IET Generation, Transmission & Distribution, 
12(5), 1052-1066. 

Kurt, M. N., Ogundijo, O., Li, C., & Wang, X. (2018). 
Online cyber-attack detection in smart grid: A 
reinforcement learning approach. IEEE Transactions 
on Smart Grid, 10(5), 5174-5185. 

Li, B., Ding, T., Huang, C., Zhao, J., Yang, Y., & Chen, 
Y. (2018). Detecting false data injection attacks 
against power system state estimation with fast go-
decomposition approach. IEEE Transactions on 
Industrial Informatics, 15(5), 2892-2904. 

https://journals.e-palli.com/home/index.php/ajsts


Pa
ge

 
8

https://journals.e-palli.com/home/index.php/ajsts

Am. J. Smart. Technol. Solutions 1(1) 1-8, 2022

Liu, C., Liang, H., Chen, T., Wu, J., & Long, C. (2019). 
Joint admittance perturbation and meter protection 
for mitigating stealthy FDI attacks against power 
system state estimation. IEEE Transactions on Power 
Systems, 35(2), 1468-1478. 

Manandhar, K., Cao, X., Hu, F., & Liu, Y. (2014). Detection 
of  faults and attacks including false data injection attack 
in smart grid using Kalman filter. IEEE transactions on 
control of  network systems, 1(4), 370-379. 

Margossian, H., Sayed, M. A., Fawaz, W., & Nakad, Z. 
(2019). Partial grid false data injection attacks against 
state estimation. International Journal of  Electrical Power 
& Energy Systems, 110, 623-629. 

Mohammadi, F., Nazri, G.-A., & Saif, M. (2019a). A 
bidirectional power charging control strategy for plug-
in hybrid electric vehicles. Sustainability, 11(16), 4317. 

Mohammadi, F., Nazri, G.-A., & Saif, M. (2019b). A fast 
fault detection and identification approach in power 
distribution systems. 2019 International Conference on 
Power Generation Systems and Renewable Energy Technologies 
(PGSRET).

Mohammadi, F., Nazri, G.-A., & Saif, M. (2019c). A real-
time cloud-based intelligent car parking system for 
smart cities. 2019 IEEE 2nd International Conference on 
Information Communication and Signal Processing (ICICSP). 

Mohammadi, F., & Neagoe, M. (2020). Emerging issues 
and challenges with the integration of  solar power 
plants into power systems. In Solar Energy Conversion in 
Communities (pp. 157-173). Springer. 

Mohammadpourfard, M., Sami, A., & Weng, Y. (2017). 
Identification of  false data injection attacks with 
considering the impact of  wind generation and 
topology reconfigurations. IEEE Transactions on 
Sustainable Energy, 9(3), 1349-1364. 

Mohammadpourfard, M., Weng, Y., Pechenizkiy, M., 
Tajdinian, M., & Mohammadi-Ivatloo, B. (2020). 
Ensuring cybersecurity of  smart grid against data 
integrity attacks under concept drift. International 
Journal of  Electrical Power & Energy Systems, 119, 
105947. 

Moslemi, R., Mesbahi, A., & Velni, J. M. (2017). A fast, 
decentralized covariance selection-based approach to 
detect cyber attacks in smart grids. IEEE Transactions 
on Smart Grid, 9(5), 4930-4941. 

Nguyen, T., Wang, S., Alhazmi, M., Nazemi, M., Estebsari, 
A., & Dehghanian, P. (2020). Electric power grid 
resilience to cyber adversaries: State of  the art. IEEE 
Access, 8, 87592-87608. 

Nikmehr, N., & Moghadam, S. M. (2019). Game-theoretic 
cybersecurity analysis for false data injection attack on 
networked microgrids. IET Cyper-Phys. Syst.: Theory & 
Appl., 4(4), 365-373. 

Ostadijafari, M., Jha, R. R., & Dubey, A. (2019). 
Conservation voltage reduction by coordinating 
legacy devices, smart inverters and battery. 2019 North 
American Power Symposium (NAPS). 

Otuoze, A. O., Mustafa, M. W., & Larik, R. M. (2018). 
Smart grids security challenges: Classification by 

sources of  threats. Journal of  Electrical Systems and 
Information Technology, 5(3), 468-483. 

Pilz, M., Naeini, F. B., Grammont, K., Smagghe, C., Davis, 
M., Nebel, J.-C., Al-Fagih, L., & Pfluegel, E. (2020). 
Security attacks on smart grid scheduling and their 
defences: a game-theoretic approach. International 
Journal of  Information Security, 19(4), 427-443. 

Sargolzaei, A., Yazdani, K., Abbaspour, A., Crane III, C. 
D., & Dixon, W. E. (2019). Detection and mitigation 
of  false data injection attacks in networked control 
systems. IEEE Transactions on Industrial Informatics, 
16(6), 4281-4292. 

Sharafeev, T., Ju, O. V., & Kulikov, A. (2018). Cyber-
security problems in smart grid cyber attacks detecting 
methods and modelling attack scenarios on electric 
power systems. 2018 International Conference on Industrial 
Engineering, Applications and Manufacturing (ICIEAM). 

Sreenath, J., Meghwani, A., Chakrabarti, S., Rajawat, K., 
& Srivastava, S. (2017). A recursive state estimation 
approach to mitigate false data injection attacks in 
power systems. 2017 IEEE Power & Energy Society 
General Meeting. 

Sreeram, T., & Krishna, S. (2019). Managing false data 
injection attacks during contingency of  secured meters. 
IEEE Transactions on Smart Grid, 10(6), 6945-6953. 

Wang, D., Wang, X., Zhang, Y., & Jin, L. (2019). Detection 
of  power grid disturbances and cyber-attacks based 
on machine learning. Journal of  Information Security and 
Applications, 46, 42-52. 

Wang, Q., Tai, W., Tang, Y., & Ni, M. (2019). Review 
of  the false data injection attack against the cyber-
physical power system. IET Cyber-Physical Systems: 
Theory & Applications, 4(2), 101-107. 

Wang, Q., Tai, W., Tang, Y., Ni, M., & You, S. (2019). A 
two-layer game theoretical attack-defense model for 
a false data injection attack against power systems. 
International Journal of  Electrical Power & Energy Systems, 
104, 169-177. 

Wang, W., & Lu, Z. (2013). Cyber security in the smart 
grid: Survey and challenges. Computer networks, 57(5), 
1344-1371. 

Xu, R., Wang, R., Guan, Z., Wu, L., Wu, J., & Du, X. 
(2017). Achieving efficient detection against false data 
injection attacks in smart grid. IEEE Access, 5, 13787-
13798. 

Yong, S. Z., Foo, M. Q., & Frazzoli, E. (2016). Robust and 
resilient estimation for cyber-physical systems under 
adversarial attacks. 2016 American Control Conference 
(ACC).

Zanetti, M., Jamhour, E., Pellenz, M., Penna, M., 
Zambenedetti, V., & Chueiri, I. (2017). A tunable fraud 
detection system for advanced metering infrastructure 
using short-lived patterns. IEEE Transactions on Smart 
Grid, 10(1), 830-840. 

Zhao, J., Mili, L., & Wang, M. (2018). A generalized 
false data injection attacks against power system 
nonlinear state estimator and countermeasures. IEEE 
Transactions on Power Systems, 33(5), 4868-4877. 

https://journals.e-palli.com/home/index.php/ajsts

