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arXiv · 2504.07478

Intelligent DoS and DDoS Detection: A Hybrid GRU-NTM Approach to Network Security

Caroline Panggabean·Chandrasekar Venkatachalam·Priyanka Shah·Sincy John·Renuka Devi P·Shanmugavalli Venkatachalam
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Abstract

Detecting Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks remains a critical challenge in cybersecurity. This research introduces a hybrid deep learning model combining Gated Recurrent Units (GRUs) and a Neural Turing Machine (NTM) for enhanced intrusion detection. Trained on the UNSW-NB15 and BoT-IoT datasets, the model employs GRU layers for sequential data processing and an NTM for long-term pattern recognition. The proposed approach achieves 99% accuracy in distinguishing between normal, DoS, and DDoS traffic. These findings offer promising advancements in real-time threat detection and contribute to improved network security across various domains.

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Caroline Panggabean, Chandrasekar Venkatachalam, Priyanka Shah, Sincy John, Renuka Devi P, Shanmugavalli Venkatachalam. 2025-04-10. Intelligent DoS and DDoS Detection: A Hybrid GRU-NTM Approach to Network Security. https://doi.org/10.1109/icosec61587.2024.10722438

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