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This study presents a hybrid data‐driven physics model‐based framework for real‐time monitoring in smart grids. As the power grid transitions to the use of smart grid technology, it's real‐time monitoring becomes more vulnerable to cyber‐attacks like false data injections (FDIs). Although smart grids cyber‐physical security has an extensive scope, this study focuses on FDI attacks, which are modelled as bad data. State‐of‐the‐art strategies for FDI detection in real‐time monitoring rely on physics model‐based weighted least‐squares state estimation solution and statistical tests. This strategy is inherently vulnerable by the linear approximation and the companion statistical modelling error, which means it can be exploited by a coordinated FDI attack. In order to enhance the robustness of FDI detection, this study presents a framework which explores the use of data‐driven anomaly detection methods in conjunction with physics model‐based bad data detection via data fusion. Multiple anomaly detection methods working at both the system level and distributed local detection level are fused. The fusion takes into consideration the confidence of the various anomaly detection methods to provide the best overall detection results. Validation considers tests on the IEEE 118‐bus system.

Modern power systems are extensively interlaced with data communication at various levels leading to increased vulnerability to cyberattacks at individual components as well as integrated controls. An effective cyber protection is, therefore, fast becoming an indispensable requirement for the smart grids. A false data injection (FDI) attack on load frequency control (LFC) is a stealth process, which has devastating consequences while at times may also lead to catastrophic system blackouts. This work comprises detailed analyses of the LFC system vulnerability to FDI attacks. It further develops an efficient event‐triggered detection scheme to leverage the LFC protection against FDI attacks. This developed event‐triggered generalised extended state observer (ET‐GESO) uses Lyapunov stability analysis to derive the event‐triggering condition and thereby reduce the communication burden significantly. The feasibility of the proposed ET‐GESO is further studied by demonstrating its Zeno behaviour exclusion. Extensive simulation studies are performed on a peer‐reported two‐area power transacting system and IEEE 39‐bus New England system. A comparison study between the proposed technique and reported Kalman filter‐based detection scheme is also performed. Different FDI attack formulations and their detection illustrate the effectiveness of the proposed detection method.

In this paper, a brief survey of measurable factors affecting the adoption of cybersecurity enhancement methods in the smart grid is provided. From a practical point of view, it is a key point to determine to what degree the cyber resilience of power systems can be improved using cost-effective resilience enhancement methods. Numerous attempts have been made to the vital resilience of the smart grid against cyber-attacks. The recently proposed cybersecurity methods are considered in this paper, and their accuracies, computational time, and robustness against external factors in detecting and identifying False Data Injection (FDI) attacks are evaluated. There is no all-inclusive solution to fit all power systems requirements. Therefore, the recently proposed cyber-attack detection and identification methods are quantitatively compared and discussed.

Complex cyber–physical networks combine the prominent features of complex networks and cyber–physical systems (CPSs), and the interconnections between the cyber layer and physical layer usually pose significant impacts on its normal operation. Many vital infrastructures, such as electrical power grids, can be effectively modeled as complex cyber–physical networks. Given the growing importance of complex cyber–physical networks, the issue of their cybersecurity has become a significant concern in both industry and academic fields. This survey is focused on some recent developments and methodologies for secure control of complex cyber–physical networks. Besides the single type of cyberattack, hybrid cyberattacks are also surveyed. The examination encompasses both cyber-only hybrid attacks and coordinated cyber–physical attacks that leverage the strengths of both physical and cyber attacks. Then, special focus will be paid to proactive secure control. Reviewing existing defense strategies from topology and control perspectives aims to proactively enhance security. The topological design allows the defender to resist potential attacks in advance, while the reconstruction process can aid in reasonable and practical recovery from unavoidable attacks. In addition, the defense can adopt active switching-based control and moving target defense strategies to reduce stealthiness, increase the cost of attacks, and limit the attack impacts. Finally, conclusions are drawn and some potential research topics are suggested.

This paper addresses multisensor multitarget tracking where the sensor network can potentially be compromised by false data injection (FDI) attacks. The existence of the targets is not known and time-varying. A tracking algorithm is proposed that can detect the possible FDI attacks over the networks. First, a local estimate is generated from the measurements of each sensor based on the labeled multi-Bernoulli (LMB) filter. Then, a detection method for FDI attacks is derived based on the Kullback–Leibler divergence (KLD) between LMB random finite set (RFS) densities. The statistical characteristics of the KLD are analyzed when the measurements are secure or compromised by FDI attacks, from which the value of the threshold is selected. Finally, the global estimate is obtained by minimizing the weighted sum of the information gains from all secure local estimates to itself. A set of suitable weight parameters is selected for the information fusion of LMB densities. An efficient Gaussian implementation of the proposed algorithm is also presented for the linear Gaussian state evolution and measurement model. Experimental results illustrate that the proposed algorithm can provide reliable tracking performance against the FDI attacks.

To alleviate the hassle of false data injection (FDI) attacks on distributed generators (DGs) in microgrids, a communication encryption-based distributed cooperative control is proposed in this paper. Compared to the conventional distributed control strategies, the proposed control scheme is simpler with much less complex evaluation mechanism by upgrading the secondary control to a second-order control based on the finite-time control theory while combining an encryption strategy. The proposed algorithm provides constant injections to eliminate the impact of FDI attacks based on a robust communication system. The effectiveness and high efficiency of the proposed control scheme is validated in an IEEE 34 Node Test Feeder system with six DGs as a microgrid cyber-physical system (CPS) under different FDI attacks.

This paper presents an approach for designing a generalized dynamic observer (GDO) aimed at detecting and isolating attack patterns that compromise the functionality of cyber–physical systems. The considered attack patterns include denial-of-service (DoS), false data injection (FDI), and random data injection (RDI) attacks. To model an attacker’s behavior and enhance the effectiveness of the attack patterns, Markovian logic is employed. The design of the generalized dynamic observer is grounded in the mathematical model of a system, incorporating its dynamics and potential attack scenarios. An attack-to-residual transfer function is utilized to establish the relationship between attack signals and the residuals generated by the observer, enabling effective detection and isolation of various attack schemes. A three-tank interconnected system, modeled under the discrete Takagi–Sugeno representation, is used as a case study to validate the proposed approach.

Accurate load prediction is critical for boosting high-quality electricity use, as well as safety in energy and power systems. However, the power system is fraught with uncertainty, and cyber-attacks on electrical loads result in inaccurate estimates. In this study, a probability density prediction method is proposed to provide reliable predictions in the face of false data injection (FDI) attacks. The method effectively integrates data-driven and statistical algorithms such as double-layer long short-term memory (DL-LSTM) networks, quantile regression (QR), and kernel density estimation (KDE). To acquire predicted values under diverse conditional quartiles, the FDI-attacked data of different types were first simulated and then utilized as the training set for the QR-DL-LSTM model. A probability density curve was drawn using the Gaussian kernel function, and interval estimates were used to more thoroughly analyze and assess predictive capability. Power load data from a wind farm in northeast China were used to confirm the availability and effectiveness of the QR-DL-LSTM model. The final results show that the proposed model has a 1.13 and 0.26 reduction in MAPE and MSE compared to the original LSTM. According to our research, the suggested model can successfully describe future power systems full of possible risks and uncertainties with great accuracy.

Technological advancement has transformed traditional vehicles into autonomous vehicles. Autonomous vehicles play an important role since they are considered an essential component of smart cities. The autonomous vehicle is an intelligent vehicle capable of maintaining safe driving by avoiding crashes caused by drivers. Unlike traditional vehicles, which are fully controlled and operated by humans, autonomous vehicles collect information about the outside environment using sensors to ensure safe navigation. Autonomous vehicles reduce environmental impact because they usually use electricity to operate instead of fossil fuel, thus decreasing the greenhouse gasses. However, autonomous vehicles could be threatened by cyberattacks, posing risks to human life. For example, researchers reported that Wi-Fi technology could be vulnerable to cyberattacks through Tesla and BMW autonomous vehicles. Therefore, further research is needed to detect cyberattacks targeting the control components of autonomous vehicles to mitigate their negative consequences. This research will contribute to the security of autonomous vehicles by detecting cyberattacks in the early stages. First, we inject False Data Injection (FDI) attacks into an autonomous vehicle simulation-based system developed by MathWorks. Inc. Second, we collect the dataset generated from the simulation model after integrating the cyberattack. Third, we implement an intelligent symmetrical anomaly detection method to identify false data cyber-attacks targeting the control system of autonomous vehicles through a compromised sensor. We utilize long short-term memory (LSTM) deep networks to detect False Data Injection (FDI) attacks in the early stage to ensure the stability of the operation of autonomous vehicles. Our method classifies the collected dataset into two classifications: normal and anomaly data. The experimental result shows that our proposed model’s accuracy is 99.95%. To this end, the proposed model outperforms other state-of-the-art models in the same study area.

One of the most prevalent and destructive types of cyber-attacks on power systems is the false data injection (FDI) attack. In a false data injection attack, the attacker inflicts large damages on the network by manipulating the measurements. The pivotal solution to opposing this type of cyber-attack is to use phasor measurement units (PMUs). In this paper, a new method is presented to confront the FDI attack by using the optimal placement of PMU instruments. In the proposed algorithm at the beginning, all PMUs placements that achieve network observability are determined using the tabu search (TS) algorithm. Then, from the observable placement vectors, the placements that minimize the possibility of a cyber-attack on the network is identified. For this purpose, a new attack criterion is presented, which is obtained from the adversary strategy in the attack scheme. Since the measurements obtained from the PMUs must be transferred to a phasor data concentrator (PDC) center, the PDC placement also must be determined. In this paper, the optimal placement of PDC is presented by considering the cost of communication infrastructure, because the cost of communication infrastructure between PMUs and PDC is significant. For this purpose, we have used the Kruskal algorithm. The simulations performed on the IEEE 30-bus and 118-bus test system confirm the effectiveness of the proposed method for opposing cyber-attacks and reducing the cost of communication infrastructure.