Detecting stealthy false data injection attacks in the smart grid using ensemble-based machine learning

Ashrafuzzaman, Mohammad; Das, Saikat; Chakhchoukh, Yacine; Shiva, Sajjan G.; Sheldon, Frederick T.

doi:10.1016/j.cose.2020.101994

Cited by 84 publications

(38 citation statements)

References 36 publications

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“…Miraftabzadeh et al [124] presented a GPR-based generalized likelihood ratio test to enhance FD performance in photovoltaic (PV) systems. In Ashrafuzzaman et al [125], two ensembles are used to detect stealthy false data injection with a supervised classifier and an unsupervised classifier. Niu et al [126] built an ensemble framework that combined five ML algorithms for power grid frequency disturbances analysis.…”

Section: Faults Detectionmentioning

confidence: 99%

“…Table 3 summarizes the AI techniques for power system FD. [134] 2017 Microgrid FD KNN, DT Garoudja et al [136] 2017 PV FD PNN Zhang et al [129] 2017 Line trip FD LSTM, SVM Sirojan et al [127] 2018 HIFD ANN Wang et al [131] 2018 Line trip FD AE, SVM Shafiullah et al [132] 2018 Microgrid FD ANN Helbing et al [138] 2018 WT FD ANN Baghaee et al [135] 2019 FD SVM Govar et al [128] 2019 HIFD ELM Jayamaha et al [133] 2019 Microgrid FD ANN Fazai et al [124] 2019 PV FD GPR Ashrafuzzaman et al [125] 2020 FD Ensemble Haq et al [130] 2020 Line FD ELM Hussain et al [137] 2020 PV FD ANN Niu et al [126] 2021 FD Ensemble Gunturi and Sarkar [139] 2021 Energy theft Ensemble…”

Section: Faults Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence Techniques in Smart Grid: A Survey

2021

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The smart grid is enabling the collection of massive amounts of high-dimensional and multi-type data about the electric power grid operations, by integrating advanced metering infrastructure, control technologies, and communication technologies. However, the traditional modeling, optimization, and control technologies have many limitations in processing the data; thus, the applications of artificial intelligence (AI) techniques in the smart grid are becoming more apparent. This survey presents a structured review of the existing research into some common AI techniques applied to load forecasting, power grid stability assessment, faults detection, and security problems in the smart grid and power systems. It also provides further research challenges for applying AI technologies to realize truly smart grid systems. Finally, this survey presents opportunities of applying AI to smart grid problems. The paper concludes that the applications of AI techniques can enhance and improve the reliability and resilience of smart grid systems.

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Section: Faults Detectionmentioning

confidence: 99%

Section: Faults Detectionmentioning

confidence: 99%

Artificial Intelligence Techniques in Smart Grid: A Survey

2021

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“…The authors in [17] propose a detection mechanism using a reinforcement learning algorithm and formulate the stealthy FDIAs detection problem as a partially observable Markov decision process. In [18], a data driven machine learning based scheme, which employs ensemble learning, is proposed to detect stealthy false data injection attacks on state estimation. Both supervised and unsupervised classification methods are used and decisions by individual classifiers are further classified [18].…”

Section: Related Workmentioning

confidence: 99%

“…In [18], a data driven machine learning based scheme, which employs ensemble learning, is proposed to detect stealthy false data injection attacks on state estimation. Both supervised and unsupervised classification methods are used and decisions by individual classifiers are further classified [18].…”

Section: Related Workmentioning

confidence: 99%

A Deviation-Based Detection Method Against False Data Injection Attacks in Smart Grid

et al. 2021

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State estimation plays a vital role to ensure safe and reliable operations in smart grid. Intelligent attackers can carefully design a destructive and stealthy false data injection attack (FDIA) sequence such that commonly used weighted least squares estimator combined with residual-based detection method is vulnerable to the FDIA. To effectively defend against an FDIA, in this paper, we propose a robust deviationbased detection method, in which an additional Kalman filter is introduced while retaining the original weighted least squares estimator, so that there are two state estimators. Moreover, an exponential weighting function is also applied to the introduced Kalman filter in our proposed method. When an FDIA occurs, the estimation results of weighted least squares estimator depend only on meter measurements at each time slot, but there is an adjustment process of estimated states for the Kalman filter based on historical states' transitions. Meanwhile, based on the exponential weighting function, estimated measurements in the Kalman filter can be adaptively suppressed for different attack strengths of FDIAs, and then the difference of the results of these two estimators increases. Subsequently, FDIAs can be effectively detected by checking the deviation of estimated measurements about the two estimators with a detection threshold. Experimental results validate the effectiveness of the proposed detection method against FDIAs. The impact of different attack strengths and noise on detection performance is also evaluated and analyzed.

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“…Besides, the limitation of those detection mechanics include intensive computation, adversary ability to predict new systems configuration, protection only limited set measurements, and modeling challenges. Ashrafuzzaman et al [ 45 ] proposed a data-driven-based mechanism to detect FDIA using an ensemble-based machine learning approach. Several classifiers are used to detect FDIA in the proposed approach.…”

Section: Background and Related Workmentioning

confidence: 99%

Rule-Based Detection of False Data Injections Attacks against Optimal Power Flow in Power Systems

Umar

Felemban

2021

Sensors

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Cyber-security of modern power systems has captured a significant interest. The vulnerabilities in the cyber infrastructure of the power systems provide an avenue for adversaries to launch cyber attacks. An example of such cyber attacks is False Data Injection Attacks (FDIA). The main contribution of this paper is to analyze the impact of FDIA on the cost of power generation and the physical component of the power systems. Furthermore, We introduce a new FDIA strategy that intends to maximize the cost of power generation. The viability of the attack is shown using simulations on the standard IEEE bus systems using the MATPOWER MATLAB package. We used the genetic algorithm (GA), simulated annealing (SA) algorithm, tabu search (TS), and particle swarm optimization (PSO) to find the suitable attack targets and execute FDIA in the power systems. The proposed FDIA increases the generation cost by up to 15.6%, 45.1%, 60.12%, and 74.02% on the 6-bus, 9-bus, 30-bus, and 118-bus systems, respectively. Finally, a rule-based FDIA detection and prevention mechanism is proposed to mitigate such attacks on power systems.

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Detecting stealthy false data injection attacks in the smart grid using ensemble-based machine learning

Cited by 84 publications

References 36 publications

Artificial Intelligence Techniques in Smart Grid: A Survey

Artificial Intelligence Techniques in Smart Grid: A Survey

A Deviation-Based Detection Method Against False Data Injection Attacks in Smart Grid

Rule-Based Detection of False Data Injections Attacks against Optimal Power Flow in Power Systems

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