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

arXiv: https://arxiv.org/abs/2504.07478 · HTML: https://mlsanity.com/paper/2504.07478/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 10 Apr 2025.

## Builds on (most cited first, 2 of 2)

- [NetFlow Datasets for Machine Learning-based Network Intrusion Detection Systems](https://mlsanity.com/paper/2011.09144/) (2020)
- [Advancing DDoS Attack Detection: A Synergistic Approach Using Deep Residual Neural Networks and Synthetic Oversampling](https://mlsanity.com/paper/2401.03116/) (2024)

## Abstract (arXiv)

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.