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Journal of Discrete Mathematical Sciences and Cryptography cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0065·ISSN (Print): 0972-0529

Monthly Journal: Publishes theoretical and applied research in all areas of Discrete Mathematical Sciences, Cryptography, Combinatorics, Elliptic Curves and Information Security.

Issues up to 2022 co-published with and available at:Taylor & Francis Online
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Open Access Research Article

Optimized deep learning methodology for intruder behavior detection and classification in cloud

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* Corresponding author · click or hover a name for details

pp. 1109–1119Vol. 26Issue 4June 2023DOI: 10.47974/JDMSC-1516 Crossmark XML
Received:
01 Jun 2022
Published Online:
21 Aug 2023
Article type:
Research Article
Language:
EN
Article no.:
JDMSC-1516
Pages:
1109–1119

Abstract

Apps, networks, frameworks, and services are all made possible by the cloud. In order to get the most out of the improved accessibility and computational capabilities, the Service Providers can offer an optimal use of current services. Application services have been revolutionised as their launch since they are useful and cost-effective for both suppliers and users. Increasingly, cyber defence is a vital research area in today’s environment, where networks are essential. The software and hardware of a network are constantly monitored by an intrusion detection framework (IDF), which is an essential part of any cyber defence plan. Many of the current IDSs are still struggling to improve detection performance, reduce false alarm rates and detect new threats. Based on parametric computation analysis, this study provides a deep learning approach to optimize cloud networks and to identify intruders. Results of suggested approach have been displayed from data compared to current methods in the conclusion section.

Keywords

Subject Classifications

68T07 (Artificial neural networks and deep learning)

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