Towards Evaluation of NIDSs in Adversarial Setting
Mohammad Hashemi, Greg Cusack, Eric R. Keller · 2019
Signature-based Network Intrusion Detection Systems (NIDSs) have traditionally been used to detect malicious traffic, but they are incapable of detecting new threats. As a result, anomaly-based NIDSs, built on neural networks, are beginning to receive attention due to their ability to seek out new attacks. However, it has been shown that neural networks are vulnerable to adversarial example attacks in other domains. But, previously proposed anomaly-based NIDSs have not been evaluated in such adversarial settings. In this paper, we show how to evaluate an anomaly-based NIDS trained on network traffic in the face of adversarial inputs. We show how to craft adversarial inputs in the highly constrained network domain, and we evaluate 3 recently proposed NIDSs in an adversarial setting.