On the practical integration of anomaly detection techniques in industrial control applications

Abstract

Despite significant advances made on anomaly detection systems, few reports are found documenting their practical integration into the industrial realm. Furthermore, the literature reports a wide range of complex detection strategies, which may require hardware changes/updates in order to be supported by critical industrial equipment such as industrial controllers (e.g., Programmable Logic Controllers). To address these issues, this paper documents a systematic methodology for the practical integration of lightweight anomaly detection algorithms into industrial control applications. It shows that industrial controllers, and in particular the scheduling rate of user programs, are sensitive to network traffic-based disturbances. Therefore, the methodology embraces the task scheduling rates found in control applications, and their deviation from the “normal” behavior. It designs a “monitoring” task, and an innovative algorithm for detecting abnormal task scheduling rates by leveraging the cumulative sum model (CUSUM) and a regression strategy applied on a specific time interval. Essentially, the approach enhances the industrial controller with a “security module” that can trigger alerts to identify early cyber attacks. The approach is extensively analyzed in the context of two industrial controllers: a Phoenix Contact ILC 350-PN controller, and a Siemens SIMATIC S7-1200 Programmable controller.