Acta Scientific Computer Sciences

Review Article Volume 3 Issue 8

Intrusion Detection Using Deep Learning Techniques in the Cloud System: A Survey

Khalid Al Makdi1,2* and Frederick T Sheldon1

1Computer Science Department, University of Idaho, Moscow, USA
2Computer Science Department, Najran University, Najran, Saudi Arabia

*Corresponding Author: Khalid Al Makdi, Computer Science Department, University of Idaho, Moscow, USA.

Received: June 24, 2021; Published: July 13, 2021

Abstract

The rapid growth of data and connectivity among computers has left the complex problem of information security. To protect data and the computer networks, numerous intrusion detection systems (IDS) have been developed that utilize machine learning (ML). However, many issues arise, especially since malicious attacks are constantly changing due to the huge volume of data stored in a distributed manner. This necessitates a scalable solution that incorporates effective feature extraction and a deep learning-based classification method. Due to the dynamic nature of malware and its continuously changing attack morphology, the malware signature datasets available publicly are updated systematically and benchmarked. This study presents a comprehensive review of IDS that uses deep learning and offers future research directions required to achieve a state-of-the-art IDS method: an objective with global security implications.

Keywords: Data Security; Deep Learning Technique; Intrusion Detection System; Cloud Database System

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Citation

Citation: Khalid Al Makdi and Frederick T Sheldon. “Intrusion Detection Using Deep Learning Techniques in the Cloud System: A Survey". Acta Scientific Computer Sciences 3.8 (2021): 17-31.

Copyright

Copyright: © 2021 Khalid Al Makdi and Frederick T Sheldon. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




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