Intelligent DoS and DDoS Detection: A Hybrid GRU-NTM Approach to Network Security
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909573720309760 |
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| author | Panggabean, Caroline Venkatachalam, Chandrasekar Shah, Priyanka John, Sincy P, Renuka Devi Venkatachalam, Shanmugavalli |
| author_facet | Panggabean, Caroline Venkatachalam, Chandrasekar Shah, Priyanka John, Sincy P, Renuka Devi Venkatachalam, Shanmugavalli |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07478 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Intelligent DoS and DDoS Detection: A Hybrid GRU-NTM Approach to Network Security Panggabean, Caroline Venkatachalam, Chandrasekar Shah, Priyanka John, Sincy P, Renuka Devi Venkatachalam, Shanmugavalli Cryptography and Security Machine Learning 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. |
| title | Intelligent DoS and DDoS Detection: A Hybrid GRU-NTM Approach to Network Security |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2504.07478 |