Application of deep learning in network security fault diagnosis and prediction
by Wang Jing; Liu Fangfang; Liu Hongyan; Wang Qingqing
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 20, No. 4, 2021

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.

Online publication date: Mon, 13-Sep-2021

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Wireless and Mobile Computing (IJWMC):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com