Fusion of Transformer and ML-CNN-BiLSTM for Network Intrusion Detection

Abstract

Network intrusion detection system (NIDS) can effectively sense network attacks, which is of great significance for maintaining the security of cyberspace. To meet the requirements of efficient and accurate network status monitoring, this paper proposes a NIDS model using deep learning network model. Firstly, GAN-Cross is used to expand minority class sample data, thereby alleviating the problem of minority class imbalance in the original dataset. Then, the Transformer module is used to adjust the ML-CNN-BiLSTM model to enhance the analysis ability of the intrusion model. Finally, the data enhancement model and feature enhancement model are integrated into the NIDS model, the detection model is optimized, the characteristics of network state data are extracted at a deeper level, and the generalization ability of the detection model is enhanced. The simulation experiments using UNSW-NB15 data sets shows that the proposed algorithm can achieve efficient analysis of complex network traffic data sets, with an accuracy of 0.903, and can effectively improve the detection accuracy of NIDS and the detection ability for unknown attacks.