Paired Swarm Optimized Relational Vector Learning for FDI Attack Detection in IoT-Aided Smart Grid

Abstract

IoT-aided smart grid heavily depends on the most innovative communication technologies that could make the grid system susceptible to False Data Injection Attacks (FDIA). The main objective of the FDI attackers remains in damaging or corrupting the state estimation strategy in the smart grid resulting in blackouts and/or to influence the electricity market. With a number of features involved in the smart grid system, FDIA detection is said to be complicated. By the conventional bad data detection systems, the FDIA detection accuracy and validation made by Receiver Operating Characteristic (ROC) curve were marginally acceptable. However, due to the time complexity and overhead incurred, the detection of FDIA is a hot research topic. In this work, we design an efficient FDIA detection method by coupling Cooperative Paired Swarm Optimization and Relational Vector Learning techniques (CPSO-RVL), to address the above-said issues. First, the cooperative paired particle swarm optimization model is proposed to attain an appropriate feature for improving the computational efficiency of FDIA detection. Next, with the obtained significant features, the relational vector learning-based FDIA detection model is designed for robust classification between FDIA and non-FDIA with minimum overhead. The extensive experiments show that the proposed method outperforms existing baseline approaches by 16%, and 34% in terms of computation time, and computation overhead respectively.