Visible to the public PGAN:A Generative Adversarial Network based Anomaly Detection Method for Network Intrusion Detection System

TitlePGAN:A Generative Adversarial Network based Anomaly Detection Method for Network Intrusion Detection System
Publication TypeConference Paper
Year of Publication2021
AuthorsLi, Zeyi, Wang, Yun, Wang, Pan, Su, Haorui
Conference Name2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)
Date Publishedoct
Keywordsanomaly detection, expert systems, generative adversarial network, generative adversarial networks, Human Behavior, human factors, intrusion detection system, Network security, privacy, pubcrawl, Real-time Systems, Scalability, supervised learning, telecommunication traffic, Traffic identification, Training, unsupervised learning
AbstractWith the rapid development of communication net-work, the types and quantities of network traffic data have in-creased substantially. What followed was the frequent occurrence of versatile cyber attacks. As an important part of network security, the network-based intrusion detection system (NIDS) can monitor and protect the network equippments and terminals in real time. The traditional detection methods based on deep learning (DL) are always in supervised manners in NIDS, which can automatically build end-to-end detection model without man-ual feature extraction and selection by domain experts. However, supervised learning methods require large-scale labeled data, yet capturing large labeled datasets is a very cubersome, tedious and time-consuming manual task. Instead, unsupervised learning is an effective way to overcome this problem. Nonetheless, the ex-isting unsupervised methods are prone to low detection efficiency and are difficult to train. In this paper we propose a novel NIDS method called PGAN based on generative adversarial network (GAN) to detect the abnormal traffic from the perspective of Anomaly Detection, which leverage the competitive speciality of adversarial training to learn the normal traffic. Based on the public dataset CICIDS2017, three experimental results show that PGAN can significantly outperform other unsupervised methods like stacked autoencoder (SAE) and isolation forest (IF).
Citation Keyli_pgangenerative_2021