Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Cyber-attacks in Cyber-Physical Microgrid Systems: A Comprehensive Review

Version 1 : Received: 20 April 2023 / Approved: 21 April 2023 / Online: 21 April 2023 (08:35:15 CEST)

How to cite: Suprabhath Koduru, S.; Machina, V.S.P.; Madichetty, S. Cyber-attacks in Cyber-Physical Microgrid Systems: A Comprehensive Review. Preprints 2023, 2023040691. https://doi.org/10.20944/preprints202304.0691.v1 Suprabhath Koduru, S.; Machina, V.S.P.; Madichetty, S. Cyber-attacks in Cyber-Physical Microgrid Systems: A Comprehensive Review. Preprints 2023, 2023040691. https://doi.org/10.20944/preprints202304.0691.v1

Abstract

Importance and need for cyber security have increased in folds since a decade. Indirectly, the country’s security depends on the country’s cyber-physical systems. Attackers are becoming more innovative, and attacks are becoming undetectable, causing huge risks to the systems. In this scenario, intelligent and evolving detection methods should be introduced to replace the basic and outworn ones. This article discusses about new-age intelligence and smart techniques dealing with artificial intelligence (AI) models. Artificial intelligence for cyber security is reviewed, and the performance of machine learning models (ML) and deep learning (DL) models are analysed. A real-time case study of stealthy local covert attacks with false data injection attacks is implemented on the DC-DC converter. A deep learning model is designed to mitigate cyber attacks, and its performance is evaluated.

Keywords

Cyber physical systems ; Cyber attacks ; Artificial Intelligence ; Machine learning ; Deep learning

Subject

Engineering, Electrical and Electronic Engineering

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