Adversarial Attack Against Dos Intrusion Detection: An Improved Boundary-Based Method

Denial of Service (DoS) attacks pose serious threats to network security. With the rapid development of machine learning technologies, artificial neural network (ANN) has been used to classify DoS attacks. However, ANN models are vulnerable to adversarial samples: inputs that are specially crafted to yield incorrect outputs. In this work, we explore a kind of DoS adversarial attacks which aim to bypass ANN-based DoS intrusion detection systems. By analyzing features of DoS samples, we propose an improved boundary-based method to craft adversarial DoS samples. The key idea is to optimize a Mahalanobis distance by perturbing continuous features and discrete features of DoS samples respectively. We experimentally study the effectiveness of our method in two trained ANN classifiers on KDDcup99 dataset and CICIDS2017 dataset. Results show that our method can craft adversarial DoS samples with limited queries.