FlexFringe：通过学习概率自动机建模软件行为
FlexFringe: Modeling Software Behavior by Learning Probabilistic Automata
AI总结：
本研究介绍了FlexFringe中概率确定性有限自动机学习方法的高效实现，通过改进状态合并策略提升性能，实验证明其在软件日志异常检测任务上优于现有神经网络方案。
摘要：
我们介绍了FlexFringe中概率确定性有限自动机学习方法的高效实现。这些实现采用了知名的状态合并策略，还包含若干改进以提升实际性能。实验表明，这些算法取得了具有竞争力的结果，相比默认实现有显著提升。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。尽管可解释性稍弱，但我们发现学习更小、更复杂的模型能提升FlexFringe在异常检测上的性能，表现优于现有基于神经网络的解决方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
