Black box attack and network intrusion detection using machine learning for malicious traffic

Yiran Zhu, Lei Cui, Zhenquan Ding, Lun Li, Yongji Liu, Zhiyu Hao

Abstract: Network intrusion detection systems (NIDS) play an important role in maintaining network security. In recent years, machine learning (ML) technology has been increasingly used in NIDS, increasing detection accuracy and aggravating network evasion risk. In order to detect the robustness of existing anomaly detection algorithms based on ML, we design and implement a black box attack method to evade network intrusion detection in this paper. The method includes two parts: first, we apply the Generative Adversarial Network (GAN) features to generate adversarial samples to generate data packets that can evade anomaly detection. Then, we insert the forged data packets into the original malicious traffic so that the anomaly detectors cannot detect the malicious traffic. We test on the latest intrusion detection datasets. Experimental results show that this method has a good evasion effect on all tested anomaly detectors, and the evasion rate of half of the anomaly detectors exceeds 90%. Through experiments, we find that the feature extraction of the existing anomaly detection algorithm has a loophole, so we propose a new defense model to make up for this loophole. Experiments confirm that the model can significantly reduce the evasion rate.