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

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any cur rent or future media. Caroline Panggabean Departement of CSE (AI) JAIN (Deemed - to - be University) Bangalore, Karnataka carolinepgabean@gmail.com Sincy John Departement of CSE (AIM) JAIN (Deemed - to - be University) Bangalore, Karnataka sincyjohn@jainuniversity.ac.in Chandrasekar Venkatachalam Departement of CSE (AI) JAIN (Deemed - to - be University) Bangalore, Karnataka chandrasekar.v@jainuniversity.ac.in Renuka Devi P Departement of CSE (AIML) JAIN (Deemed - to - be University) Bangalore, Karnataka renukadevi.p@jainuniversity.ac.in Priyanka Shah Departement of CSE (AI) JAIN (Deemed - to - be University) Bangalore, Karnataka priyankashah8324@gmail.com Shanmugavalli Venkatachalam Department of CSE KSR College of Engineering Namakkal, Tamil N adu drvshanmugavalli@gmail.com 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.