RANet: Network intrusion detection with group-gating convolutional neural network

Zhang，Xiaoqing1,2; Yang，Fei2; Hu，Yue2; Tian，Zhao2; Liu，Wei2; Li，Yifa2; She，Wei2,3

2022-02-01

10.1016/j.jnca.2021.103266

JOURNAL OF NETWORK AND COMPUTER APPLICATIONS   影响因子和分区

1084-8045

1095-8592

With the rapid increase of human activities in cyberspace, various network intrusions are tended to be frequent and hidden. Network intrusion detection (NID) has attracted more and more attention from industrial and academic fields. Over the years, researchers have developed artificial intelligence methods to tackle them. However, most existing methods are usually not feasible and sustainable when faced with the demands of current NID systems. To alleviate this problem, this paper proposes a novel convolutional neural network (CNN) named RANet for NID automatically. In RANet, we not only introduce a Group-Gating module but also apply the overlapping method to the last max-pooling layer. Based on the hyper-parameter settings of our RANet, a lot of performance comparison experiments are conducted. The results demonstrate that RANet achieves better NID performance than strong baselines and existing state-of-the-art methods on five publicly available NID benchmarks. For example, the RANet improves accuracy with approximately 5.3% on NSL-KDD Test dataset through comparisons to state-of-the-art baselines. Moreover, the results of RANet also indicate that it has the great potential or use in current NID systems.

Convolutional neural network Group-Gating module Network intrusion detection RANet

SCI ; EI

英语

第一 ; 通讯

Key Laboratory Open Project Fund of Engineering and Technical Research Center of Embankment Safety and Disease Control of Ministry of Water Resources[2018007] ; National Key R&D Program of China[2018YFB1201403] ; Natural Science Foundation of China[61602422] ; CERNET Innovation Project[NGII20180702]

Computer Science

Computer Science, Hardware & Architecture ; Computer Science, Interdisciplinary Applications ; Computer Science, Software Engineering

WOS:000724984100001

ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD

20214811237727

Benchmarking ; Convolution ; Convolutional neural networks ; Statistical tests

Information Theory and Signal Processing:716.1 ; Computer Software, Data Handling and Applications:723 ; Mathematical Statistics:922.2

COMPUTER SCIENCE

2-s2.0-85119916153

Scopus

1.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,China
2.School of Software,Zhengzhou University,Zhengzhou University,China
3.Research Center on Levee Safety Disaster Prevention,Zhengzhou,China

Zhang，Xiaoqing,Yang，Fei,Hu，Yue,et al. RANet: Network intrusion detection with group-gating convolutional neural network[J]. JOURNAL OF NETWORK AND COMPUTER APPLICATIONS,2022,198.

Zhang，Xiaoqing.,Yang，Fei.,Hu，Yue.,Tian，Zhao.,Liu，Wei.,...&She，Wei.(2022).RANet: Network intrusion detection with group-gating convolutional neural network.JOURNAL OF NETWORK AND COMPUTER APPLICATIONS,198.

Zhang，Xiaoqing,et al."RANet: Network intrusion detection with group-gating convolutional neural network".JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 198(2022).