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Recent advances in artificial intelligence and the increasing need forpowerful defensive measures in the domain of network security, have led to theadoption of deep learning approaches for use in network intrusion detectionsystems. These methods have achieved superior performance against conventionalnetwork attacks, which enable the deployment of practical security systems tounique and dynamic sectors. Adversarial machine learning, unfortunately, hasrecently shown that deep learning models are inherently vulnerable toadversarial modifications on their input data. Because of this susceptibility,the deep learning models deployed to power a network defense could in fact bethe weakest entry point for compromising a network system. In this paper, weshow that by modifying on average as little as 1.38 of the input features, anadversary can generate malicious inputs which effectively fool a deep learningbased NIDS. Therefore, when designing such systems, it is crucial to considerthe performance from not only the conventional network security perspective butalso the adversarial machine learning domain.

Rallying Adversarial Techniques against Deep Learning for Network Security