论文标题
Flexfringe：通过学习概率自动机对软件行为进行建模
FlexFringe: Modeling Software Behavior by Learning Probabilistic Automata
论文作者
论文摘要
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有效实现。这些实施了众所周知的国家合并策略，包括几项修改以提高其在实践中的绩效。我们通过实验表明，这些算法获得了竞争成果，并且对默认实施的实现有了重大改进。我们还演示了如何使用FlexFringe从软件日志中学习可解释的模型并将其用于异常检测。尽管不容易解释，但我们表明，学习较小的更复杂的模型可以提高弹性方面的弹性检测性能，从而优于基于神经网的现有解决方案。
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.
