  Anomaly detection is essential in many application domains, such as cyber
security, law enforcement, medicine, and fraud protection. However, the
decision-making of current deep learning approaches is notoriously hard to
understand, which often limits their practical applicability. To overcome this
limitation, we propose a framework for learning inherently interpretable
anomaly detectors from sequential data. More specifically, we consider the task
of learning a deterministic finite automaton (DFA) from a given multi-set of
unlabeled sequences. We show that this problem is computationally hard and
develop two learning algorithms based on constraint optimization. Moreover, we
introduce novel regularization schemes for our optimization problems that
improve the overall interpretability of our DFAs. Using a prototype
implementation, we demonstrate that our approach shows promising results in
terms of accuracy and F1 score.
