FDI attack detection using extra trees algorithm and deep learning algorithm-autoencoder in smart grid

Majidi, Seyed Hossein; Hadayeghparast, Shahrzad; Karimipour, Hadis

doi:10.1016/j.ijcip.2022.100508

Cited by 45 publications

(11 citation statements)

References 19 publications

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“…Problems with fault detection and event monitoring are regarded as classification models. SVM [12] and rule-based learning [13] techniques are used to resolve this. The approach proposed by work [14] employs RSSI to forecast the link quality.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Smart Grid Security Based on Blockchain with Industrial Fault Detection Using Wireless Sensor Network and Deep Learning Techniques

Kandasamy,

Anto,

Baranitharan

et al. 2023

Journal of Sensors

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Low-cost monitoring and automation solutions for smart grids have been made viable by recent advancements in embedded systems and wireless sensor networks (W.S.N.s). A well-designed smart network of subsystems and metasystems known as a “smart grid” is aimed at enhancing the conventional power grid’s efficiency and guaranteeing dependable energy delivery. A smart grid (S.G.) requires two-way communication between utility providers and end users in order to accomplish its aims. This research proposes a novel technique in enhancing the smart grid security and industry fault detection using a wireless sensor network with deep learning architectures. The smart grid network security has been enhanced using a blockchain-based smart grid node routing protocol with IoT module. The industrial analysis has been carried out based on monitoring for fault detection in a network using Q-learning-based transfer convolutional network. The experimental analysis has been carried out in terms of bit error rate, end-end delay, throughput rate, spectral efficiency, accuracy, M.A.P., and RMSE. The proposed technique attained bit error rate of 65%, end-end delay of 57%, throughput rate of 97%, spectral efficiency of 93%, accuracy of 95%, M.A.P. of 55%, and RMSE of 75%. This proposed paradigm is advantageous for the operation of smart grids for increased security and industrial fault detection across the network because security is the biggest barrier in smart grid implementation.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Smart Grid Security Based on Blockchain with Industrial Fault Detection Using Wireless Sensor Network and Deep Learning Techniques

Kandasamy,

Anto,

Baranitharan

et al. 2023

Journal of Sensors

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“…Using deep learning as auxiliaries [52,66,72,74] Simply developing classifiers for detection [53,54,69,76,77,81] Locating false data injection attacks [55,56,60,63,68,75] Resorting to deep reinforcement learning for detection [64,65,79,95] Detecting attacks with specific targets [57,70,78,80] Addressing the problem of attack samples insufficiency [58,59,67,83] Considering disturbances from renewable energy integration [60,61] Handling the privacy problem in constructing detectors [62,71,73] [51] designed novel FDIA strategies by introducing adversarial samples (also called perturbation vectors) into FDIAs, thereby deceiving BDDs and DL-based detectors.…”

Section: Classification Literaturementioning

confidence: 99%

“…[69] proposed a GCN framework to analyze the graphical aspect of FDIAs by exploiting the graphical structures of the power network. A stacked AE was designed in [76] along with an extremely randomised tree classifier to address FDIA issues. Ref.…”

Section: Review Of Deep Learning Applications For Cybersecurity In Sm...mentioning

confidence: 99%

Deep learning for cybersecurity in smart grids: Review and perspectives

Ruan

Liang

Zhao

et al. 2023

Energy Conversion and Econom

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Protecting cybersecurity is a non‐negotiable task for smart grids (SG) and has garnered significant attention in recent years. The application of artificial intelligence (AI), particularly deep learning (DL), holds great promise for enhancing the cybersecurity of SG. Nevertheless, previous surveys and review articles have failed to comprehensively investigate the intersection between DL and SG cybersecurity. To address this gap, this study presents a survey of the latest advancements in DL technology and their relevance to SG cybersecurity. First, the functional mechanisms and scope of application of common DL techniques are explored. Subsequently, SG cyberthreats are categorised into distinct types of cyberattacks that have not been systematically examined in previous surveys. Based on this, a thorough review of the application of DL techniques in addressing each cyberthreat along with recommendations and a generalised framework for enhancing cyberattack detection using DL is offered. Finally, insights are provided into the emerging challenges presented by DL applications in SG cybersecurity that are yet to be widely acknowledged, and potential research avenues are proposed to address or alleviate these challenges.

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“…Considering that the gain 𝐾 𝑑𝑒𝑓 = 𝐾 H−2 = 𝑊𝑋 −1 , ( 7) and ( 9) now become ( 13) and ( 14), and the optimization problem to reduce the disturbance impact can be written as ( 11)- (14).…”

Section: H-2 and H-∞ Controller Designmentioning

confidence: 99%

“…if there exists a scalar 𝛼 > 0. After substituting the value of 𝛽 derived in (35) back into (31), we apply Schur's complement lemma [47], to rearrange the inequality containing 𝛼 −1 into the equivalent inequality in (36) which would replace constraint (14) in the original H-2 formulation making it a robust H-2 controller capable of handling uncertainty in the feedback loop.…”

Section: Robust Controller Under Uncertain Ev Feedback Loadmentioning

confidence: 99%

Protecting the future grid: An electric vehicle robust mitigation scheme against load altering attacks on power grids

Sayed,

Ghafouri,

Atallah

et al. 2023

Applied Energy

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FDI attack detection using extra trees algorithm and deep learning algorithm-autoencoder in smart grid

Cited by 45 publications

References 19 publications

[Retracted] Smart Grid Security Based on Blockchain with Industrial Fault Detection Using Wireless Sensor Network and Deep Learning Techniques

[Retracted] Smart Grid Security Based on Blockchain with Industrial Fault Detection Using Wireless Sensor Network and Deep Learning Techniques

Deep learning for cybersecurity in smart grids: Review and perspectives

Protecting the future grid: An electric vehicle robust mitigation scheme against load altering attacks on power grids

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