An Extreme Semi-supervised Framework Based on Transformer for Network Intrusion Detection
Yangmin Li, Xinhang Yuan, Wengen Li
Abstract
Network intrusion detection (NID) aims to detect various network attacks and is an important task for guaranteeing network security. However, existing NID methods usually require a large amount of labeled data for training, which is impractical in many real application scenarios due to the high cost. To address this issue, we proposed an extreme semi-supervised framework based on transformer (ESet) for NID. ESeT first developed a multi-level feature extraction module to learn both packet-level byte encoded features and flow-level frequency domain features to enrich the information for detection. Then, during the semi-supervised learning, ESeT designed the dual-encoding transformer to fuse the extracted features for intrusion detection and introduced the credibility selector to reduce the negative impacts of incorrect pseudo-labeling of unlabeled data. The experiment results show that our model achieves excellent performance (F1-score: 97.60%) with only a small proportion of labeled data (1%) on CIC-IDS2017 and CSE-CIC-IDS2018 datasets.