  Recent advances in artificial intelligence and the increasing need for
powerful defensive measures in the domain of network security, have led to the
adoption of deep learning approaches for use in network intrusion detection
systems. These methods have achieved superior performance against conventional
network attacks, which enable the deployment of practical security systems to
unique and dynamic sectors. Adversarial machine learning, unfortunately, has
recently shown that deep learning models are inherently vulnerable to
adversarial modifications on their input data. Because of this susceptibility,
the deep learning models deployed to power a network defense could in fact be
the weakest entry point for compromising a network system. In this paper, we
show that by modifying on average as little as 1.38 of the input features, an
adversary can generate malicious inputs which effectively fool a deep learning
based NIDS. Therefore, when designing such systems, it is crucial to consider
the performance from not only the conventional network security perspective but
also the adversarial machine learning domain.
