Visible to the public Detecting Adversarial Examples for Network Intrusion Detection System with GAN

TitleDetecting Adversarial Examples for Network Intrusion Detection System with GAN
Publication TypeConference Paper
Year of Publication2020
AuthorsPeng, Y., Fu, G., Luo, Y., Hu, J., Li, B., Yan, Q.
Conference Name2020 IEEE 11th International Conference on Software Engineering and Service Science (ICSESS)
Date Publishedoct
Keywordsadversarial examples detection, adversarial sample, bidirectional generative adversarial network, computer network security, data distribution, defense technology, gan, Generative Adversarial Learning, generative adversarial networks, Generators, intelligent network intrusion detection system, learning (artificial intelligence), machine learning, machine learning algorithms, network and data security, network intrusion detection, network intrusion system, neural nets, Predictive Metrics, pubcrawl, Resiliency, Scalability, security, Training
AbstractWith the increasing scale of network, attacks against network emerge one after another, and security problems become increasingly prominent. Network intrusion detection system is a widely used and effective security means at present. In addition, with the development of machine learning technology, various intelligent intrusion detection algorithms also start to sprout. By flexibly combining these intelligent methods with intrusion detection technology, the comprehensive performance of intrusion detection can be improved, but the vulnerability of machine learning model in the adversarial environment can not be ignored. In this paper, we study the defense problem of network intrusion detection system against adversarial samples. More specifically, we design a defense algorithm for NIDS against adversarial samples by using bidirectional generative adversarial network. The generator learns the data distribution of normal samples during training, which is an implicit model reflecting the normal data distribution. After training, the adversarial sample detection module calculates the reconstruction error and the discriminator matching error of sample. Then, the adversarial samples are removed, which improves the robustness and accuracy of NIDS in the adversarial environment.
Citation Keypeng_detecting_2020