ABATe: Automatic Behavioral Abstraction Technique to Detect Anomalies in Smart Cyber-Physical Systems

Detecting anomalies and attacks in smart cyber-physical systems are of paramount importance owing to their growing prominence in controlling critical systems. However, this is a challenging task due to the heterogeneity and variety of components of a CPS, and the complex relationships between sensed values and potential attacks or anomalies. Such complex relationships are results of physical constraints and domain norms which exist in many CPS domains. In this article, we propose ABATe, an Automatic Behavioral Abstraction Technique based on neural networks for detecting anomalies in smart cyber-physical systems. Unlike traditional techniques which abstract the statistical properties of different sensor values, ABATe learns complex relationships between event vectors from normal operational data available in abundance with smart CPS and uses this abstracted model to detect anomalies. ABATe detected more than 88 percent of attacks in the publicly available SWaT dataset featuring data from a scaled down sewage water treatment plant with a very low false positive rate of 1 percent. We also evaluated our technique's ability to capture domain semantics and multi-domain adaptability using a real-world automotive dataset, as well as a synthetic dataset.