An Improved Rough Set Theory Based Feature Selection Approach for Intrusion Detection in SCADA Systems

Despite the increasing awareness of cyber-attacks against Critical Infrastructure (CI), safeguarding the Supervisory Control and Data Acquisition (SCADA) systems remains inadequate. For this purpose, designing an efficient SCADA Intrusion Detection System (IDS) becomes a significant research topic of the researchers to counter cyber-attacks. Most of the existing works present several statistical and machine learning approaches to prevent the SCADA network from the cyber-attacks. Whereas, these approaches failed to concern the most common challenge, "Curse of dimensionality". This scenario accentuates the necessity of an efficient feature selection algorithm in SCADA IDS where it identifies the relevant features and eliminates the redundant features without any loss of information. Hence, this paper proposes a novel filter-based feature selection approach for the identification of informative features based on Rough Set Theory and Hyper-clique based Binary Whale Optimization Algorithm (RST-HCBWoA). Experiments were carried out by Power system attack dataset and the performance of RST-HCBWoA was evaluated in terms of reduct size, precision, recall, classification accuracy, and time complexity.