We present the efficient implementations of probabilistic deterministic finite automaton learning methods available in FlexFringe. These implement well-known strategies for state-merging including several modifications to improve their performance in practice. We show experimentally that these algorithms obtain competitive results and significant improvements over a default implementation. We also demonstrate how to use FlexFringe to learn interpretable models from software logs and use these for anomaly detection. Although less interpretable, we show that learning smaller more convoluted models improves the performance of FlexFringe on anomaly detection, outperforming an existing solution based on neural nets.

翻译：我们介绍了FlexFringe中可用的概率确定性有限自动机学习方法的高效实现。这些实现采用了著名的状态合并策略，并包含了若干改进以提升其实际性能。实验表明，这些算法相较于默认实现获得了具有竞争力的结果和显著的性能提升。我们还展示了如何使用FlexFringe从软件日志中学习可解释模型，并将其用于异常检测。尽管可解释性有所降低，但学习更小且更复杂的模型能提升FlexFringe在异常检测中的性能，甚至优于基于神经网络的现有解决方案。