Effect of time patterns in mining process invariants for industrial control systems: an experimental study

Effect of time patterns in mining process invariants for industrial control systems: an experimental study

연구 분야: Infrastructure

학회: SEA4DQ 2022: Proceedings of the 2nd International Workshop on Software Engineering and AI for Data Quality in Cyber-Physical Systems/Internet of Things

Machine Learning is playing a crucial role in the design of intrusion detectors for Industrial Control Systems (ICS). Intrusion Detection Systems (IDS) rely on data obtained from an operational ICS. Such datasets contain multiple time series, one for each process variable. In this work, we explore how such time series can be exploited to understand the effect of time patterns in mining the process invariants, i.e., conditions on process state variables. We use the knowledge gained through the time patterns to determine the optimal data collection size for generating the invariants. The study reported here was conducted using the operational data obtained from a water treatment plant.