Detecting Stealthy False Data Injection Attacks in Power Grids Using Deep Learning

Ashrafuzzaman, Md.; Chakhchoukh, Yacine; Jillepalli, Ananth A.; Tošić, Predrag T.; Leon, Daniel Conte de; Sheldon, Frederick T.; Johnson, Brian K.

doi:10.1109/iwcmc.2018.8450487

Cited by 52 publications

(23 citation statements)

References 31 publications

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“…Notably, the algorithms presented in [31, 103, 107] are thoroughly tested on multiple power system architectures, such as IEEE 14, 30, and 118 bus systems, contrary to the algorithms in [57, 99, 102], which utilise only one IEEE system model during the performance analysis. Additionally, some papers consider basic FDIAs while others evaluate detection performance against stealthy FDIAs, which significantly skews the algorithm efficacy [97, 125]. Finally, a number of researchers develop their custom metrics to assess the proposed detection algorithms or do not provide any quantitative results whatsoever.…”

Section: Discussion On the Detection Performance Of Machine Learning mentioning

confidence: 99%

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

Hu²,

et al. 2020

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Section: Discussion On the Detection Performance Of Machine Learning mentioning

confidence: 99%

“…Ashrafuzzaman et al [125] proposed different MLP structures for the detection of FDIAs in an AC static SE system topology. The paper assumes that partial knowledge of the system, including the

H

matrix and other parameters, is known to the attacker.…”

Section: Machine Learning For Fdias Detectionmentioning

confidence: 99%

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

Hu²,

et al. 2020

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“…In this paper, the results demonstrated that the proposed approach not only requires less assumption on system topologies and attack types, but also verifies the high detection accuracy of the adopted DL. Reference [187] compared the performance of three different DL approaches: (i) gradient boosting machines (GBM), (ii) generalized linear modelings (GLM), and (iii) distributed random forests (DRF). The numerical results justified that DL-based approaches can accurately detect FDIA scenarios against SE algorithms.…”

Section: Detection Using Machine Learningmentioning

confidence: 99%

Electric Power Grid Resilience to Cyber Adversaries: State of the Art

et al. 2020

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The smart electricity grids have been evolving to a more complex cyber-physical ecosystem of infrastructures with integrated communication networks, new carbon-free sources of power generation, advanced monitoring and control systems, and a myriad of emerging modern physical hardware technologies. With the unprecedented complexity and heterogeneity in dynamic smart grid networks comes additional vulnerability to emerging threats such as cyber attacks. Rapid development and deployment of advanced network monitoring and communication systems on one hand, and the growing interdependence of the electric power grids to a multitude of lifeline critical infrastructures on the other, calls for holistic defense strategies to safeguard the power grids against cyber adversaries. In order to improve the resilience of the power grid against adversarial attacks and cyber intrusions, advancements should be sought on detection techniques, protection plans, and mitigation practices in all electricity generation, transmission, and distribution sectors. This survey discusses such major directions and recent advancements from a lens of different detection techniques, equipment protection plans, and mitigation strategies to enhance the energy delivery infrastructure resilience and operational endurance against cyber attacks. This undertaking is essential since even modest improvements in resilience of the power grid against cyber threats could lead to sizeable monetary savings and an enriched overall social welfare. INDEX TERMS Cyber physical systems (CPS), cyber attack, intrusion detection system (IDS), false data injection attack (FDIA), energy management system (EMS), power grid resilience.

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“…Ashrafuzzaman et al . [38] proposed a simple deep learning‐based model for FDI attack detection using Multi‐Layer Perceptron Neural Network (MLPNN) and compare the attack detection capabilities of their scheme with other machine learning approaches on IEEE‐14 bus system. The proposed scheme fails to capture both spatial correlations of the measurements and temporal variations in the data even when the load profile used is not realistic.…”

Section: Background Informationmentioning

confidence: 99%