Automating Security Incident Response in SCADA Systems through SIEM-ML Integration

The increasing cybersecurity threats to Supervisory Control and Data Acquisition (SCADA) systems in critical infrastructure sectors have raised significant concerns. Traditional security approaches are struggling to keep up with the rapidly evolving threat landscape, emphasizing the need for more robust and automated incident response capabilities. This paper aims to tackle this challenge by suggesting integrating Security Information and Event Management (SIEM) systems with Machine Learning (ML) techniques to improve the detection, analysis, and response to security incidents in SCADA environments. This work has two main contributions. First, it shows how integrating SIEM and ML can enhance the detection of evolving security risks in industrial settings. By using ML algorithms within the SIEM framework, the proposed approach can effectively identify and respond to emerging threats. Second, the study develops a predictive threat detection platform that uses historical data and real-time incident analysis to predict and address potential security issues within SCADA systems. The experimental results showcase the efficacy of this SIEM-ML integration in improving incident response capabilities. The findings emphasize the value of including contextual SCADA information in ML-driven frameworks to provide comprehensive security solutions for critical infrastructure sectors. This work offers a structured approach to strengthening the defenses of SCADA systems against the dynamic and complex cyber threat landscape.