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Network intrusion attacks are a known threat. To detect such attacks, networkintrusion detection systems (NIDSs) have been developed and deployed. Thesesystems apply machine learning models to high-dimensional vectors of featuresextracted from network traffic to detect intrusions. Advances in NIDSs havemade it challenging for attackers, who must execute attacks without beingdetected by these systems. Prior research on bypassing NIDSs has mainly focusedon perturbing the features extracted from the attack traffic to fool thedetection system, however, this may jeopardize the attack's functionality. Inthis work, we present TANTRA, a novel end-to-end Timing-based AdversarialNetwork Traffic Reshaping Attack that can bypass a variety of NIDSs. Ourevasion attack utilizes a long short-term memory (LSTM) deep neural network(DNN) which is trained to learn the time differences between the targetnetwork's benign packets. The trained LSTM is used to set the time differencesbetween the malicious traffic packets (attack), without changing their content,such that they will "behave" like benign network traffic and will not bedetected as an intrusion. We evaluate TANTRA on eight common intrusion attacksand three state-of-the-art NIDS systems, achieving an average success rate of99.99\% in network intrusion detection system evasion. We also propose a novelmitigation technique to address this new evasion attack.

TANTRA: Timing-Based Adversarial Network Traffic Reshaping Attack