Journal
ELECTRONICS
Volume 12, Issue 5, Pages -Publisher
MDPI
DOI: 10.3390/electronics12051255
Keywords
network security; deep learning; network intrusion detection; domain adaptation; transfer learning
Ask authors/readers for more resources
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.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available