TANTRA: Timing-Based Adversarial Network Traffic Reshaping Attack

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 endto-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.

Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review

“…Sharon et al [49] presented TANTRA, a timing-based adversarial network traffic reshaping attack that reshapes malicious traffic using timestamp attributes in order to evade detection without affecting the packet's content. The approach was assessed using the Kitsune and CIC-IDS2017 datasets over the KitNET, an advanced NIDS, Autoencoder, Isolation Forest, and achieved a success rate of 99.99%.…”

The Threat of Offensive AI to Organizations

“…Bots trying to contact their C2 server can generate URLs that appear legitimate to humans [175], or that can evade malicious-URL detectors [208]. To evade traffic-based NIDSs, adversaries can shape their traffic [85,166] or change their timing to hide it [200].…”