Advancing Intrusion Detection: Application of Distributed Deep Learning on the KDD Cup 99 Dataset

International Journal of Electronics and Communication Engineering

© 2024 by SSRG - IJECE Journal

Volume 11 Issue 6

Year of Publication : 2024

Authors : Agalit Mohamed Amine, El Youness Idrissi Khamlichi

10.14445/23488549/IJECE-V11I6P109

How to Cite?

Agalit Mohamed Amine, El Youness Idrissi Khamlichi, "Advancing Intrusion Detection: Application of Distributed Deep Learning on the KDD Cup 99 Dataset," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 6, pp. 107-113, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I6P109

Abstract:

Intrusion Detection Systems (IDS) are crucial for protecting IT infrastructures against increasingly sophisticated and evolving threats. Faced with complex attacks such as stealthy or polymorphic threats, conventional methods based on rules or signatures show their limitations. An innovative IDS approach utilizing a deep neural network integrated into a distributed architecture for dynamic and precise network traffic analysis is introduced. Tested on the KDD Cup 99 dataset, this method demonstrated an accuracy of 99.90%, a recall of 99.89%, and a specificity of 100%, marking a significant improvement over traditional IDS systems. The exceptional performance obtained encourages the broader adoption of this system and suggests significant potential for revolutionizing IT security practices. The implications of the findings for current security strategies are also discussed, and directions for future research are proposed.

Keywords:

Intrusion detection, Deep learning, Distributed IDS architecture, KDD Cup 99, Cybersecurity.

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