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

Caroline Panggabean, Chandrasekar Venkatachalam, Priyanka Shah, Sincy John, Renuka Devi P +1 more
4/10/2025
cs.CRcs.LG

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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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Cite this paper

@article{panggabean2025intelligent,
  title  = {Intelligent DoS and DDoS Detection: A Hybrid GRU-NTM Approach to Network Security},
  author = {Caroline Panggabean and Chandrasekar Venkatachalam and Priyanka Shah and Sincy John and Renuka Devi P and Shanmugavalli Venkatachalam},
  year   = {2025},
  eprint = {2504.07478},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2504.07478v1}
}

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