Liuer Mihou: A Practical Framework for Generating and Evaluating Grey-box Adversarial Attacks against NIDS. (arXiv:2204.06113v1 [cs.CR])

Due to its high expressiveness and speed, Deep Learning (DL) has become an
increasingly popular choice as the detection algorithm for Network-based
Intrusion Detection Systems (NIDSes). Unfortunately, DL algorithms are
vulnerable to adversarial examples that inject imperceptible modifications to
the input and cause the DL algorithm to misclassify the input. Existing
adversarial attacks in the NIDS domain often manipulate the traffic features
directly, which hold no practical significance because traffic features cannot
be replayed in a real network. It remains …

adversarial attacks box framework