Year: 2024
Type: conference-paper
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 UNSW-NB15 and BoT-IoT datasets, the model employs GRU layers for sequential data processing and an NTM for long-term pattern recognition. The model achieves 99% accuracy in distinguishing between normal, DoS, and DDoS traffic. This approach offers promising advancements in realtime threat detection, potentially improving network security across various domains.
Authors: Caroline Panggabean, Chandrasekar Venkatachalam, Priyanka Shah, Sincy John, P Renuka Devi, Shanmugavalli Venkatachalam
Institution: Jain University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4403722866