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Deep Learning for Intrusion Detection: A Game Changer
1 SBIT Inc.
2 Department of Computer Science and Engineering, Daffodil International University Dhaka Bangladesh.
3 Computer Science and Engineering, Stamford University Bangladesh.
4 Masters in Computer Science, Louisiana State University in Shreveport.
5 Computer Science, Louisiana State University in Shreveport.
6 Department of Computer Science, American International University.
Research Article
International Journal of Science and Research Archive, 2024, 13(02), 1574-1585.
Article DOI: 10.30574/ijsra.2024.13.2.2563
Publication history:
Received on 13 November 2024; revised on 23 December 2024; accepted on 29 December 2024
Abstract:
This study explores the application of deep learning techniques in intrusion detection systems (IDS) and evaluates their potential to revolutionize cybersecurity. The conventional IDS techniques are usually ineffective against advanced and dynamic cyber threat, and thus, the number of security breaches is increasing. A promising solution can be deep learning that is capable of analyzing complex patterns and learning based on big data sets. This research demonstrates that deep learning models, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), significantly improve detection accuracy, reduce false positives, and enhance real-time threat identification. Significant results indicate the effectiveness of deep learning-based IDS over the conventional rule-based systems with a significant rise in the detection of the past undetected threats. The paper arrives at the conclusion that the incorporation of deep learning into IDS is a game changer as it can provide solid defence against the new cyber threats and clear the path towards more adaptive and intelligent security-related actions.
Keywords:
Intrusion Detection; Deep Learning; Cybersecurity Threats; Detection Accuracy; False Positives; Real-Time Response
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
