Stealthy data integrity attack identification in smart grid networks utilizing deep denoising autoencoder

Smart grids arose as the largest cyber-physical systems with the integration of sophisticated control, computing, and state-of-the-art communications. Like all cyber-physical systems, the smart grids are vulnerable to malicious cyber assaults due to their enormous dependency on communication network...

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Vydané v:Heliyon Ročník 10; číslo 19; s. e38470
Hlavní autori: Kousar, Anila, Ahmed, Saeed, Altamimi, Abdullah, Kim, Su Min, Khan, Zafar A.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England Elsevier Ltd 15.10.2024
Elsevier
Predmet:
Cyber assaults
Cyber-physical systems
Deep denoising autoencoder
electric power
electrical equipment
evolution
industry
Machine learning
Smart grids
State estimation
support vector machines
Cyber-physical systems
Smart grids
Cyber assaults
State estimation
Deep denoising autoencoder
Machine learning
ISSN:2405-8440, 2405-8440
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Shrnutí:Smart grids arose as the largest cyber-physical systems with the integration of sophisticated control, computing, and state-of-the-art communications. Like all cyber-physical systems, the smart grids are vulnerable to malicious cyber assaults due to their enormous dependency on communication networks. Various machine learning-based schemes are being investigated in the industry and academia to develop robust defense mechanisms to counter cyber assaults. However, the curse of high dimensionality, which increases with the escalating evolution of an electric power system, infringes upon the efficiency of machine learning models employed to detect such assaults. To this end, this paper proposes a deep denoising autoencoder (DAE)-based framework for dimensionality reduction that learns salient feature representation for high-dimensional, multi-variant smart grid measurement data collected from the smart grids. The latent space apprehended by DAE is then fed to binary support vector machine (SVM) to determine the assaulted data. Various standard IEEE test cases are employed in simulations. The results show that the proposed scheme learns more robust features that reveal the nonlinear properties exhibited in the smart grid measurements, further leading to improved detection accuracy of the classifier as compared to existing approaches.
Bibliografia:ObjectType-Article-1
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content type line 23
ISSN:2405-8440
2405-8440
DOI:10.1016/j.heliyon.2024.e38470