Survey of machine learning methods for detecting false data injection attacks in power systems

Sayghe, Ali; Hu, Yiwei; Zografopoulos, Ioannis; Liu, Xiaorui; Dutta, Raj Gautam; Jin, Yier; Konstantinou, Charalambos

doi:10.1049/iet-stg.2020.0015

Cited by 109 publications

(54 citation statements)

References 132 publications

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“…Since large-power systems present several stochastic conditions to be analyzed in such fashion, a data-driven approach is used in [15], for non-synchronous generation impact analysis. False injection attacks also uses data-driven controllers to detect anomalies in the power systems data as mentioned in [78]. Data-driven electromechanical oscillations controllers are part of such development [79], where oscillations-data paths can be identified for power systems monitoring [80].…”

Section: Discussionmentioning

confidence: 99%

Data-Driven Trajectory Prediction of Grid Power Frequency Based on Neural Models

et al. 2021

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Frequency in power systems is a real-time information that shows the balance between generation and demand. Good system frequency observation is vital for system security and protection. This paper analyses the system frequency response following disturbances and proposes a data-driven approach for predicting it by using machine learning techniques like Nonlinear Auto-regressive (NAR) Neural Networks (NN) and Long Short Term Memory (LSTM) networks from simulated and measured Phasor Measurement Unit (PMU) data. The proposed method uses a horizon-window that reconstructs the frequency input time-series data in order to predict the frequency features such as Nadir. Simulated scenarios are based on the gradual inertia reduction by including non-synchronous generation into the Nordic 32 test system, whereas the PMU collected data is taken from different locations in the Nordic Power System (NPS). Several horizon-windows are experimented in order to observe an adequate margin of prediction. Scenarios considering noisy signals are also evaluated in order to provide a robustness index of predictability. Results show the proper performance of the method and the adequate level of prediction based on the Root Mean Squared Error (RMSE) index.

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Section: Discussionmentioning

confidence: 99%

Data-Driven Trajectory Prediction of Grid Power Frequency Based on Neural Models

et al. 2021

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“…Sayghe et al [ 44 ] presented a survey of machine learning methods to detect FDIA in power systems. The authors presented comprehensive background information on FDIA, the impact of FDIA on power systems, and the FDIA defense mechanisms that utilize the machine learning approach to detect FDIA in power systems.…”

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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“…Cybersecurity landscape on electricity market, operations, generation systems, distribution, customer, and service provider domains has been also described in [25]. A literature survey of machine learning approaches for the detection of false data injection attacks on power systems is provided in [7]. Survey of FDI attacks including load redistribution attacks, smart meter data manipulation, and economic operation are also provided.…”

Section: Introductionmentioning

confidence: 99%

“…Survey of FDI attacks including load redistribution attacks, smart meter data manipulation, and economic operation are also provided. The work presented in [7] describes comprehensively how machine learning algorithms such as supervised learning, semi-supervised, unsupervised learning, and deep learning are used to detect FDI attacks. Literature review of impact of FDI attacks on economic dispatch, state estimation, and distributed control of distributed generators is provided in [26].…”

Section: Introductionmentioning

confidence: 99%

Detection of Cyber Attacks on Voltage Regulation in Distribution Systems Using Machine Learning

2021

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Several wired and wireless advanced communication technologies have been used for coordinated voltage regulation schemes in distribution systems. These technologies have been employed to both receive voltage measurements from field sensors and transmit control settings to voltage regulating devices (VRDs). Communication networks for voltage regulation can be susceptible to data falsification attacks, which can lead to voltage instability. In this context, an attacker can alter multiple field measurements in a coordinated manner to disturb voltage control algorithms. This paper proposes a machine learning-based two-stage approach to detect, locate, and distinguish coordinated data falsification attacks on control systems of coordinated voltage regulation schemes in distribution systems with distributed generators. In the first stage (regression), historical voltage measurements along with current meteorological data (solar irradiance and ambient temperature) are provided to random forest regressor to forecast voltage magnitudes of a given current state. In the second stage, a logistic regression compares the forecasted voltage with the measured voltage (used to set VRDs) to detect, locate, and distinguish coordinated data falsification attacks in realtime. The proposed approach is validated through several case studies on a 240-node real distribution system (based in the USA) and the standard IEEE 123-node benchmark distribution system. The results show that the proposed approach can detect low margin attacks (as low as 1% of actual measurements) with up to 99% accuracy. All of the developed source codes of the proposed solution are publicly available at Github.https://github.com/nbhusal/Data-Attack-on-Voltage-Regulation.INDEX TERMS Coordinated control of voltage regulation, data falsification cyber attack, machine learning, and photovoltaic.

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Survey of machine learning methods for detecting false data injection attacks in power systems

Cited by 109 publications

References 132 publications

Data-Driven Trajectory Prediction of Grid Power Frequency Based on Neural Models

Data-Driven Trajectory Prediction of Grid Power Frequency Based on Neural Models

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

Detection of Cyber Attacks on Voltage Regulation in Distribution Systems Using Machine Learning

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