Comprehensive Cybersecurity Risk Assessment Framework for Industrial Control Systems (ICS) and SCADA Environments: Identifying Threats, Vulnerabilities, and Mitigation Strategies

Abstract

In the face of rapidly evolving cyber threats, traditional security systems have proven inadequate for protecting critical infrastructure environments such as Industrial Control Systems (ICS). This study investigates the integration of machine learning (ML) techniques and cybersecurity knowledge graphs (CKGs) to enhance threat detection and situational awareness in complex cyber-physical ecosystems. By evaluating advanced context-aware detection models, including LSTM-Autoencoders and hybrid SVM-based systems, the research demonstrates significant improvements in anomaly detection accuracy and interpretability. Furthermore, the study explores how knowledge graphs—constructed from heterogeneous data sources—facilitate semantic reasoning and automate threat intelligence processing. Empirical evidence highlights the value of combining behavioral attributes with semantic context to identify and mitigate sophisticated attacks that evade conventional intrusion detection systems (IDS). Despite these advancements, the research also identifies operational challenges, including system integration, computational overhead, and limited scalability of AI models in real-world deployments. Recommendations are made for future implementation strategies that prioritize interoperability, explainability, and collaboration among interdisciplinary teams. Finally, potential directions for further research are proposed, such as the adoption of blockchain for secure data provenance and the expansion of CKG frameworks into domains like finance and smart healthcare. This work contributes to the growing body of knowledge advocating for intelligent, adaptive, and semantically enriched cybersecurity systems capable of responding proactively to dynamic threat landscapes.