  Network intrusion attacks are a known threat. To detect such attacks, network
intrusion detection systems (NIDSs) have been developed and deployed. These
systems apply machine learning models to high-dimensional vectors of features
extracted from network traffic to detect intrusions. Advances in NIDSs have
made it challenging for attackers, who must execute attacks without being
detected by these systems. Prior research on bypassing NIDSs has mainly focused
on perturbing the features extracted from the attack traffic to fool the
detection system, however, this may jeopardize the attack's functionality. In
this work, we present TANTRA, a novel end-to-end Timing-based Adversarial
Network Traffic Reshaping Attack that can bypass a variety of NIDSs. Our
evasion attack utilizes a long short-term memory (LSTM) deep neural network
(DNN) which is trained to learn the time differences between the target
network's benign packets. The trained LSTM is used to set the time differences
between the malicious traffic packets (attack), without changing their content,
such that they will "behave" like benign network traffic and will not be
detected as an intrusion. We evaluate TANTRA on eight common intrusion attacks
and three state-of-the-art NIDS systems, achieving an average success rate of
99.99\% in network intrusion detection system evasion. We also propose a novel
mitigation technique to address this new evasion attack.
