Abstract
Protecting industrial control systems against cyberattacks is crucial to counter escalating threats to critical infrastructure. To this end, Industrial Intrusion Detection Systems (IIDSs) provide an easily retrofittable approach to uncover attacks quickly and before they can cause significant damage. Current research focuses either on maximizing automation, usually through heavy use of machine learning, or on expert systems that rely on detailed knowledge of the monitored systems. While the former hinders the interpretability of alarms, the latter is impractical in real deployments due to excessive manual work for each individual deployment. To bridge the gap between maximizing automation and leveraging expert knowledge, we introduce GeCo, a novel IIDS based on automatically derived comprehensible models of benign system behavior. GeCo leverages state-space models mined from historical process data to minimize manual effort for operators while maintaining high detection performance and generalizability across diverse industrial domains. Our evaluation against state-of-the-art IIDSs and datasets demonstrates GeCo’s superior performance while remaining comprehensible and performing on par with expert-derived rules. GeCo represents a critical step towards empowering operators with control over their cybersecurity toolset, thereby enhancing the protection of valuable physical processes in industrial control systems and critical infrastructures.
Konrad Wolsing, M.Sc.
External Researcher / FKIE
Eric Wagner, M.Sc.
External Researcher / FKIE
Luisa Lux
