A statechart-based anomaly detection model for multi-threaded SCADA systems

Abstract

SCADA traffic between the Human Machine Interface (HMI) and the Programmable Logic Controller (PLC) is known to be highly periodic. However, it is sometimes multiplexed, due to asynchronous scheduling. Modeling the network traffic patterns of multiplexed SCADA streams using Deterministic Finite Automata (DFA) for anomaly detection typically produces a very large DFA, and a high false-alarm rate. In this paper we introduce a new modeling approach that addresses this gap. Our Statechart DFA modeling includes multiple DFAs, one per cyclic pattern, together with a DFA-selector that de-multiplexes the incoming traffic into sub-channels and sends them to their respective DFAs. We evaluated our solution on traces from a production SCADA system using the Siemens S7-0x72 protocol. We also stress-tested our solution on a collection of synthetically-generated traces. In all but the most extreme scenarios the Statechart model drastically reduced both the false-alarm rate and the learned model size in comparison with the naive single-DFA model.