Robust Attack Graph Generation. (arXiv:2206.07776v1 [cs.LG])

We present a method to learn automaton models that are more robust to input
modifications. It iteratively aligns sequences to a learned model, modifies the
sequences to their aligned versions, and re-learns the model. Automaton
learning algorithms are typically very good at modeling the frequent behavior
of a software system. Our solution can be used to also learn the behavior
present in infrequent sequences, as these will be aligned to the frequent ones
represented by the model. We apply our …

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