Intelligent DoS and DDoS Detection: A Hybrid GRU-NTM Approach to Network Security
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.
Cite
@article{arxiv.2504.07478,
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},
journal= {arXiv preprint arXiv:2504.07478},
year = {2025}
}
Comments
Accepted at the 2024 5th International Conference on Smart Electronics and Communication (ICOSEC). This is the accepted manuscript version. The final version is published by IEEE at https://doi.org/10.1109/ICOSEC61587.2024.10722438