Metaheuristics based dimensionality reduction with deep learning driven false data injection attack detection for enhanced network security.

Abstract

Recent sensor, communication, and computing technological advancements facilitate smart grid use. The heavy reliance on developed data and communication technology increases the exposure of smart grids to cyberattacks. Existing mitigation in the electricity grid focuses on protecting primary or redundant measurements. These approaches make certain assumptions regarding false data injection (FDI) attacks, which are inadequate and restrictive to cope with cyberattacks. The reliance on communication technology has emphasized the exposure of power systems to FDI assaults that can bypass the current bad data detection (BDD) mechanism. The current study on unobservable FDI attacks (FDIA) reveals the severe threat of secured system operation because these attacks can avoid the BDD method. Thus, a Data-driven learning-based approach helps detect unobservable FDIAs in distribution systems to mitigate these risks. This study presents a new Hybrid Metaheuristics-based Dimensionality Reduction with Deep Learning for FDIA (HMDR-DLFDIA) Detection technique for Enhanced Network Security. The primary objective of the HMDR-DLFDIA technique is to recognize and classify FDIA attacks in the distribution systems. In the HMDR-DLFDIA technique, the min-max scalar is primarily used for the data normalization process. Besides, a hybrid Harris Hawks optimizer with a sine cosine algorithm (hybrid HHO-SCA) is applied for feature selection. For FDIA detection, the HMDR-DLFDIA technique utilizes the stacked autoencoder (SAE) method. To improve the detection outcomes of the SAE model, the gazelle optimization algorithm (GOA) is exploited. A complete set of experiments was organized to highlight the supremacy of the HMDR-DLFDIA method. The comprehensive result analysis stated that the HMDR-DLFDIA technique performed better than existing DL models.