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

Caroline Panggabean, Chandrasekar Venkatachalam, Priyanka Shah, Sincy John, Renuka Devi P, Shanmugavalli Venkatachalam

2025-04-10Intrusion Detection
PaperPDF

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.

Related Papers

CyberRAG: An agentic RAG cyber attack classification and reporting tool2025-07-03Detection of Cyber Attack in Network using Machine Learning Techniques.2025-07-02Generative Adversarial Evasion and Out-of-Distribution Detection for UAV Cyber-Attacks2025-06-26Poster: Enhancing GNN Robustness for Network Intrusion Detection via Agent-based Analysis2025-06-25KnowML: Improving Generalization of ML-NIDS with Attack Knowledge Graphs2025-06-24Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS20172025-06-23Dynamic Temporal Positional Encodings for Early Intrusion Detection in IoT2025-06-22On the Performance of Cyber-Biomedical Features for Intrusion Detection in Healthcare 5.02025-06-19