A Network Intrusion Detection Method Based on Domain Confusion-Reference-Cited by-同舟云学术

A Network Intrusion Detection Method Based on Domain Confusion

Published:2023-03-06 Issue:5 Volume:12 Page:1255
ISSN:2079-9292
Container-title:Electronics
language:en
Short-container-title:Electronics

Author:

Qu Yanze¹^ORCID,Ma Hailong²,Jiang Yiming²,Bu Youjun²

Affiliation:

1. Institute of Information Technology, PLA Information Engineering University, Zhengzhou 450003, China

2. National Digital Switching System Engineering and Technological Research Center, Zhengzhou 450001, China

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.

Funder

National Natural Science Fund of China

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

Link

https://www.mdpi.com/2079-9292/12/5/1255/pdf

Reference21 articles.

1. A systematic literature review of methods and datasets for anomaly-based network intrusion detection;Yang;Comput. Secur.,2022

2. Sommer, R., and Paxson, V. (2010, January 16–19). Outside the closed world: On using machine learning for network intrusion detection. Proceedings of the 2010 IEEE Symposium on Security and Privacy, Berleley/Oakland, CA, USA.

3. Generative adversarial networks;Goodfellow;Commun. ACM,2020

4. Bontemps, L., Cao, V.L., McDermott, J., and Le-Khac, N.A. (2016, January 23–25). Collective anomaly detection based on long short-term memory recurrent neural networks. Proceedings of the International Conference on Future Data and Security Engineering, Can Tho City, Vietnam.

5. Kang, M.J., and Kang, J.W. (2017, January 13–16). Method of intrusion detection using deep neural network. Proceedings of the 2017 IEEE International Conference on Big Data and Smart Computing (BigComp), Jeju Island, Republic of Korea.

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Research on Network Intrusion Detection Based on Cluster Learning Algorithm;2024 3rd International Conference for Innovation in Technology (INOCON);2024-03-01

2. Applying Feature Transformation-Based Domain Confusion to Neural Network for the Denoising of Dispersion Spectrograms;Seismological Research Letters;2023-10-17

3. On the Robustness of ML-Based Network Intrusion Detection Systems: An Adversarial and Distribution Shift Perspective;Computers;2023-10-17

4. Machine Learning-Based Intrusion Detection for Rare-Class Network Attacks;Electronics;2023-09-16