Precision DDoS Detection through Gaussian Noise-Augmented Neural Networks

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.