Intelligent DoS and DDoS Detection: A Hybrid GRU-NTM Approach to Network Security
This addresses network security threats for cybersecurity applications, but it is incremental as it builds on existing deep learning methods.
The paper tackled the problem of detecting DoS and DDoS attacks in cybersecurity by proposing a hybrid deep learning model combining GRUs and an NTM, achieving 99% accuracy on UNSW-NB15 and BoT-IoT datasets.
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.