Model-Based Anomaly Detection for Discrete Event Systems

Abstract

Model-based anomaly detection in technical systems is an important application field of artificial intelligence. We consider discrete event systems, which is a system class to which a wide range of relevant technical systems belong and for which no comprehensive model-based anomaly detection approach exists so far. The original contributions of this paper are threefold: First, we identify the types of anomalies that occur in discrete event systems and we propose a tailored behavior model that captures all anomaly types, called probabilistic deterministic timed-transition automata (PDTTA). Second, we present a new algorithm to learn a PDTTA from sample observations of a system. Third, we describe an approach to detect anomalies based on a learned PDTTA. An empirical evaluation in a practical application, namely ATM fraud detection, shows promising results.