TANTRA: Timing-Based Adversarial Network Traffic Reshaping Attack

TL;DR

TANTRA is a timing-based adversarial attack that uses LSTM neural networks to modify packet timing in network traffic, successfully evading intrusion detection systems without altering packet content.

Contribution

This work introduces TANTRA, a novel timing-based attack leveraging LSTM to bypass NIDSs by mimicking benign traffic timing, and proposes a mitigation method.

Findings

Achieved 99.99% success rate in evading NIDSs.

Effective across multiple attack types and detection systems.

Introduced a mitigation technique against timing-based evasion.

Abstract

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.