Unsupervised Automata Learning via Discrete Optimization

TL;DR

This paper introduces a new framework for learning deterministic finite automata from unlabeled data using constraint optimization, addressing a gap in unsupervised automata learning and demonstrating its practical use in anomaly detection.

Contribution

It proposes the first methods for unsupervised DFA learning from unlabeled data, including three algorithms and regularization schemes to enhance interpretability.

Findings

Algorithms are practically feasible for anomaly detection.

Regularization improves DFA interpretability.

Learning from unlabeled data is computationally hard.

Abstract

Automata learning is a successful tool for many application domains such as robotics and automatic verification. Typically, automata learning techniques operate in a supervised learning setting (active or passive) where they learn a finite state machine in contexts where additional information, such as labeled system executions, is available. However, other settings, such as learning from unlabeled data - an important aspect in machine learning - remain unexplored. To overcome this limitation, we propose a framework for learning a deterministic finite automaton (DFA) from a given multi-set of unlabeled words. We show that this problem is computationally hard and develop three 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 practical feasibility in the context of unsupervised anomaly detection.