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

• Published in: Energies (Basel) 2023-06, Vol.16 (12), p.4573
• Main Authors: Suprabhath Koduru, Sriranga, Machina, Venkata Siva Prasad, Madichetty, Sreedhar
• Format: Article
• Language: English
• Subjects: Access to information; Alternative energy sources; Artificial intelligence; Communication; Computer viruses; Controllers; cyber attacks; Cyber-physical systems; Cybersecurity; Cyberterrorism; Data integrity; Data security; deep learning; Distributed generation; Electronic warfare; Energy resources; Industrial plant emissions; Information technology; Internet of Things; machine learning; Malware; Military strategy; Neural networks; Nuclear energy; Nuclear power plants; Pattern recognition; Renewable resources; Safety and security measures; Safety measures; Sensors; Structured Query Language-SQL; War; Work stations
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en16124573

The importance of and need for cyber security have increased in the last decade. The critical infrastructure of the country, modeled with cyber-physical systems (CPS), is becoming vulnerable because of a lack of efficient safety measures. Attackers are becoming more innovative, and attacks are becoming undetectable, thereby causing huge risks to these systems. In this scenario, intelligent and evolving detection methods should be introduced to replace basic and outworn methods. The ability of artificial intelligence (AI) to analyze data and predict outcomes has created an opportunity for researchers to explore the power of AI in cyber security. This article discusses new-age intelligence and smart techniques such as pattern recognition models, deep neural networks, generative adversarial networks, and reinforcement learning for cyber security in CPS. The differences between the traditional security methods used in information technology and the security methods used in CPS are analyzed, and the need for a transition into intelligent methods is discussed in detail. A deep neural network-based controller that detects and mitigates cyber attacks is designed for microgrid systems. As a case study, a stealthy local covert attack that overcomes the existing microgrid protection is modeled. The ability of the DNN controller to detect and mitigate the SLCA is observed. The experiment is performed in a simulation and also in real-time to analyze the effectiveness of AI in cyber security.