Enhancing Cybersecurity Through Combined Convolutional Neural Network-Gated Recurrent Unit Approach for Distributed Denial of Service Attack Detection

In cyber-security, integrating GRU with CNN has proven to be a solid and effective method for detecting Distributed Denial of Service (DDoS) attacks. This work presents a robust approach that combines the temporal sequence modelling capabilities of GRUs with the spatial feature extraction capabilities of CNNs. Integrating these two deep learning architectures provides a comprehensive solution, allowing precise detection and reduction of harmful activity inside network traffic. This work enhances DDoS detection methods and emphasizes the increasing importance of using advanced deep learning techniques to strengthen cyber-security measures. The suggested methodology effectively tackles the dynamic nature of DDoS attacks by leveraging the strengths of CNNs and GRUs. This approach offers a diverse and efficient way to protect network integrity. Integrating CNN and GRU in DDoS detection is crucial in strengthening network security against emerging attacks in the ever-changing digital ecosystem. This study highlights the flexibility and effectiveness of incorporating sophisticated neural network structures to improve the robustness of cyber-security systems in response to changing threats.

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