一句话：FlexFringe 提供高效的概率确定性有限自动机学习实现，可从软件日志学习可解释模型并用于异常检测。
该文介绍 FlexFringe 中概率确定性有限自动机（PDFA）学习方法的实现。这些方法基于状态合并策略，并包含多项改进以提升实际性能。作者通过实验表明，这些算法取得有竞争力的结果，并显著优于默认实现。论文还展示如何从软件日志中学习可解释模型，用于异常检测；虽然可解释性较低，但学习更小、更复杂的模型能提升 FlexFringe 在异常检测上的表现，并超过基于神经网络的现有方案。
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.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@article{abs-2203-16331,
title = {FlexFringe: Modeling Software Behavior by Learning Probabilistic Automata},
author = {Sicco Verwer and Christian Hammerschmidt},
journal = {Logical Methods in Computer Science},
volume = {Volume 21, Issue 3},
year = {2025},
doi = {10.46298/lmcs-21(3:31)2025}
}