A Network Intrusion Detection Method Based on Domain Confusion

Network intrusion detection models based on deep learning encounter problems in the migration application. The performance is not as good as expected. In this paper, a network intrusion detection method based on domain confusion is proposed to improve the migration performance of the model. A domain confusion network is designed for feature transformation based on the idea of domain adaptation, mapping the traffic data in different network environments to the same feature space. Meanwhile, a regularizer is proposed to control the information loss in the mapping process to ensure that the transformed feature obtains enough information for intrusion detection. The experiment results show that the detection performance of the model in this paper is similar to or even better than the traditional models, and the migration performance in different network environments is better than the traditional models.

Automated summary

In this paper, a network intrusion detection method based on domain confusion is proposed to improve the migration performance of deep learning models. It utilizes a domain confusion network for feature transformation, mapping traffic data in different network environments to the same feature space. Experiment results demonstrate that the proposed method achieves comparable or even better detection performance compared to traditional models, and shows better migration performance in different network environments.