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