Data-Driven Cyber-Attack Detection of Intelligent Attacks in Islanded DC Microgrids

In this letter, a data-driven cyber-attack detection method for islanded dc microgrids is proposed. Data are collected by monitoring the behavior of an intelligent attacker who is able to bypass the conventional cyber-attack detection algorithms and disrupt the operation of the system. The reinforcement learning algorithm emulates the actions of such intelligent attacker, who exploits the vulnerability of index-based cyber-attack detection methods, such as discordant detection algorithm. The data are then used to train a neural-network-based detector that complements the conventional method with additional capability to detect a larger set of possible attacks. Through experiments, the effectiveness of the proposed method is validated.

Automated summary

In this paper, a data-driven cyber-attack detection method for islanded dc microgrids is proposed. The method collects data by monitoring the behavior of an intelligent attacker who can bypass conventional detection algorithms. A reinforcement learning algorithm is used to emulate the attacker's actions, who exploits the vulnerability of index-based detection methods. The collected data is then used to train a neural-network-based detector that complements the conventional method.