Intrusion Detection Systems for Cyber Attacks Detection in Power Line Communications Networks

Power Line Communication (PLC) is categorized into wired and wireless technologies to distribute the power and transmit the data at different frequency ranges. System administration is one of the significant area in these networks to manage communication processes. Security is one of the significant concern which make networks slow and unavailable, false and altered instructions exist, malfunctioning, and abnormal behavior of systems observed. Intrusion Detection System (IDS) is one of the solution to handle security attacks and protect the systems from unauthorized access. However, the existing IDS systems have limited capabilities to handle the new attacks. This paper proposes a Machine Learning (ML) algorithm for IDS system used in PLC networks to improve the overall system performance and detect the vulnerabilities of the system. The proposed system can detect the latest assaults and protect the systems from unauthorized and malicious activities. The proposed IDS system is assessed by using a virtual environment using the latest dataset and compared with existing traditional systems. The experiment results indicated the better performance of the proposed system to handle the new assaults and protect the systems.