An Effective Deep Learning Based Scheme for Network Intrusion Detection

Hongpo Zhang, Chase Q. Wu, Shan Gao, Zongmin Wang, Yuxiao Xu, Yongpeng Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

Intrusion detection systems (IDS) play an important role in the protection of network operations and services. In this paper, we propose an effective network intrusion detection scheme based on deep learning techniques. The proposed scheme employs a denoising autoencoder (DAE) with a weighted loss function for feature selection, which determines a limited number of important features for intrusion detection to reduce feature dimensionality. The selected data is then classified by a compact multilayer perceptron (MLP) for intrusion identification. Extensive experiments are conducted on the UNSW-NB dataset to demonstrate the effectiveness of the proposed scheme. With a small feature selection ratio of 5.9%, the proposed scheme is still able to achieve a superior performance in terms of different evaluation criteria. The strategic selection of a reduced set of features yields satisfactory detection performance with low memory and computing power requirements, making the proposed scheme a promising solution to intrusion detection in high-speed networks.

Original languageEnglish (US)
Title of host publication2018 24th International Conference on Pattern Recognition, ICPR 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages682-687
Number of pages6
ISBN (Electronic)9781538637883
DOIs
StatePublished - Nov 26 2018
Event24th International Conference on Pattern Recognition, ICPR 2018 - Beijing, China
Duration: Aug 20 2018Aug 24 2018

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume2018-August
ISSN (Print)1051-4651

Other

Other24th International Conference on Pattern Recognition, ICPR 2018
Country/TerritoryChina
CityBeijing
Period8/20/188/24/18

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

Keywords

  • Intrusion detection system
  • deep learning
  • denoising autoen-coder
  • feature selection

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