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Precision DDoS Detection through Gaussian Noise-Augmented Neural Networks
Conference proceeding   Peer reviewed

Precision DDoS Detection through Gaussian Noise-Augmented Neural Networks

Ali Alfatemi, Diogo Oliveira, Mohamed Rahouti, Abdelatif Hafid and Nasir Ghani
International Conference on the Network of the Future (Online), pp.178-185
International Conference on Network of the Future (NoF), 15th (Castelldefels, Spain, 10/02/2024–10/04/2024)
11/2024
DOI: https://doi.org/10.1109/NoF62948.2024.10741358
Web of Science ID: WOS:001413192000030

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Abstract

Deep learning Distributed Denial of Service Gaussian noise Cybersecurity Electronic Neural Network Machine Learning
The detection of Distributed Denial of Service (DDoS) attacks is a critical challenge in network security, requiring effective and efficient solutions to safeguard data and services. This paper addresses this problem by introducing a neural network model specifically designed for DDoS attack detection. The model employs a streamlined architecture to ensure rapid processing times and high performance. A key innovation is the integration of Gaussian noise, which enhances the robustness and generalization capabilities of the model. Extensive experiments validate the effectiveness and resilience of this approach, demonstrating its practicality for real-world network security applications. The findings highlight the significant role of noise regularization in improving the reliability of neural network models for detecting cyber threats.

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