Title: Application of deep learning in network security fault diagnosis and prediction

Authors: Wang Jing; Liu Fangfang; Liu Hongyan; Wang Qingqing

Addresses: College of Optical and Electronical Information, Changchun University of Science and Technology, Changchun 130000, Jilin, China ' College of Optical and Electronical Information, Changchun University of Science and Technology, Changchun 130000, Jilin, China ' Network Management Centre of China Mobile Communication Group Jilin Co., Ltd., Changchun 130012, Jilin, China ' Jilin Animation Institute, Changchun 130012, Jilin, China

Abstract: At present, deep learning method has been successfully applied in many application directions, but few researchers try to apply deep learning to network security fault diagnosis. This paper summarises the deep learning methods applied to network security fault diagnosis and prediction, and focuses on the attack detection using stacked automatic encoder. The network data sets are used to compare various attacks. The fault diagnosis process based on the deep learning method and the analysis and verification of the experimental results are introduced in detail. At the same time, the automatic operation time is implemented in order to monitor and predict the network application characteristics and deep learning mechanism, intrusion detection system can be used to monitor network applications and send out an alarm when an attack is detected.

Keywords: deep learning; network security; fault diagnosis; automatic encoder.

DOI: 10.1504/IJWMC.2021.117570

International Journal of Wireless and Mobile Computing, 2021 Vol.20 No.4, pp.381 - 389

Received: 24 Nov 2020
Accepted: 01 Apr 2021
Published online: 13 Sep 2021 *

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