Securing SCADA Systems: A Protocol-Based Intrusion Detection Approach with Shapley Analysis

This paper presents an approach to enhancing the security of Supervisory Control and Data Acquisition (SCADA) systems through the development of Explainable Intrusion Detection Systems (X-IDS). SCADA systems play a crucial role in managing industrial processes and critical infrastructure, yet they are increasingly targeted by cyberattacks, posing significant risks to operational integrity. Leveraging Machine Learning (ML) techniques, particularly focused on network protocols involved in communication with SCADA HMI, this study proposes a protocol-based layered IDS framework to mitigate potential threats. Evaluation of the proposed models demonstrates promising results, achieving high accuracy rates surpassing previous studies. The performance metrics, including accuracy, precision, recall, and F1 scores, along with Brier scores, are comprehensively analyzed to gauge the effectiveness of the models. Additionally, Explainable Machine Learning (XAI) techniques such as SHAP values provide transparent insights into the model's decision-making process. The study highlights the importance of securing HMIs within SCADA systems and offers valuable insights for future research directions in enhancing overall network security.