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

Abstract

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.

Exploring the efficacy of GRU model in classifying the signal to noise ratio of microgrid model

“…Networked microgrids (NMGs) are considered one of the most critical parts of microgrid implementation due to their ability to operate, monitor, and control distributed energy resources. Communication channels typically experience noise interruptions, specifically in noisy environments, which can prevent NMG components from exchanging information effectively 33 . Such disturbances often originate from switching power supplies, silicon-controlled rectifiers, brush motors, dimmer switches, and industrial equipment, all directly connected to the supply network 34 .…”

A Review of AI-Based Cyber-Attack Detection and Mitigation in Microgrids

“…A significant percentage of consumers would lose power due to such malfunctions and cyber-attacks, and very sensitive and mission-critical equipment may suffer serious harm [10][11][12][13][14][15]. Table 1 summarizes the actual reported cyber-attacks on the energy industry [16][17][18]. After examining reported cyber-attacks on the energy sector, a typical cyber-attack chain is found to be initiated by gaining initial access through spear phishing.…”