SAIN: Improving ICS Attack Detection Sensitivity via State-Aware Invariants

Industrial Control Systems (ICSs) rely on Programmable Logic Controllers (PLCs) to operate within a set of states. The states are composed of variables that determine how sensor data is interpreted, configuration parameters are applied, and actuator commands are issued. Recent works have shown that attackers can manipulate these variables to compromise ICS safety and security. To detect such attacks, previous approaches have leveraged invariants-a set of rules defining the correct behavior of an ICS. However, these invariants suffer from a critical limitation: they are state-agnostic. This means they define variable ranges across all possible ICS states, leading to loosely bounded detection thresholds. Unfortunately, attackers can exploit these loose bounds and launch stealthy attacks that evade detection without violating such invariants. In this paper, we introduce SAIN, an automated method to derive state-aware ICS invariants with tighter bounds and enforce them through a PLC-based monitor. SAIN first generates invariant templates by identifying the PLC program states, state transitions, and the inter-dependencies among sensing, actuation, and configuration variables within each state through program analysis. It then partitions the ICS data traces into state-specific sub-traces and quantifies the invariant templates with concrete, tighter bounds, as system-specific knowledge about the subject ICS. Lastly, it enforces the state-aware invariants through a run-time monitor. We evaluate SAIN on a Fischertechnik manufacturing plant and a chemical plant simulator against 17 attacks. SAIN protects the plants, on average, with a false positive rate of 2% and a run-time overhead of 3%.