Attacking DNN-based Intrusion Detection Models. Issue 5 (2020)

• Record Type: Journal Article
• Authors: Zhang, Xingwei; Zheng, Xiaolong; Wu, Desheng Dash
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 5(2020)
• Page Start: 415
• Page End: 419
• Publication Date: 2020
• Subjects: public security -- intrusion detection -- deep neural networks -- adversarial perturbations; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2021.04.118
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Intrusion detection plays an important role in public security domains. Dynamic deep neural network(DNN)-based intrusion detection models have been demonstrated to show effective performance for timely detecting network intrusions. While DNN-based intrusion detection models have shown powerful performance, in this paper, we verify that they could be easily attacked by well-designed small adversarial perturbations. We design an effective procedure to employ commonly used adversarial perturbations for attacking well-trained DNN detection models on NSL-KDD dataset. We further find that the performance of DNN models for recognizing real labels of abnormal data suffers more from attacks compared with that on normal samples.