Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
