Detecting the Cyber Attacks for Distributed Systems Using Machine Learning Algorithms

Cyber-physical system security for electric distribution systems is critical. In direct switching attacks, often coordinated, attackers seek to toggle remote-controlled switches in the distribution network. Due to the typically radial operation, certain configurations may lead to outages and/or voltage violations. Existing optimization methods that model the interactions between the attacker and the power system operator (defender) assume knowledge of the attacker’s parameters. This reduces their usability. Furthermore, the trend with coordinated cyber attack detection has been the use of centralized mechanisms, correlating data from dispersed security systems. This can be prone to single point failures. In this paper, novel mathematical models are presented for the attacker and the defender. The models do not assume any knowledge of the attacker’s parameters by the defender. Instead, a machine learning (ML) technique implemented by a multi-agent system correlates detected attacks in a decentralized manner, predicting the targets of the attacker. Furthermore, agents learn optimal mitigation of the communication level through Q-learning. The learned attacker motive is also used by the defender to determine a new configuration of the distribution network. Simulations of the technique have been performed using the IEEE 123-Node Test Feeder. The simulation results validate the capability and performance of the algorithm.”Detecting Cyber Attacks in Distributed Systems Using Machine Learning Algorithm” presents a solution to the problem of identifying cyber attacks in distributed systems. As distributed systems are becoming increasingly complex, traditional methods of detecting cyber attacks have become insufficient. In this paper, the authors propose a machine learning-based approach that can identify cyber attacks in distributed systems. The approach involves collecting data from various sources, preprocessing the data, and using machine learning algorithms to classify the data as normal or an attack. The proposed approach was evaluated on a real-world datasets and showed promising results. The findings of this paper have implications for improving the security of distributed systems, which are critical for the functioning of many organizations.