2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 14 , Issue 1 , PP: 278-292, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Israa Ali Al-Neami 1 * , Zaynab Saeed Hameed 2 , Zahraa Abbas Al-zubaydi 3
Doi: https://doi.org/10.54216/JISIoT.140122
The rapidly evolving landscape of cyber threats demands robust and adaptive Intrusion Detection Systems (IDS) capable of real-time operation. This paper presents a novel approach to augmenting Field-Programmable Gate Arrays (FPGA) for the development of a high-performance IDS designed to enhance communication security by rapidly and accurately identifying threats. The proposed system integrates advanced techniques, including Meta Ensemble Learning (MEL), Extreme Gradient Boosting (XGBoost), and a Hybrid Deep Learning (HDL) model that combines Long Short-Term Memory (LSTM) networks for temporal analysis and Convolutional Neural Networks (CNN) for feature extraction. This synergistic approach significantly reduces detection latency and improves the accuracy of threat identification. The effectiveness of the FPGA-based IDS is evaluated using four widely recognized datasets—NSL-KDD, IoTID20, CICIDS2017, and UNSW NB15—all of which focus on communication attacks, making them ideal for testing IDS performance in diverse IoT environments. The results demonstrate that the proposed IDS not only achieves a high detection rate with a low false positive rate but also operates efficiently in real-time settings, underscoring its viability as a critical security solution in data communication networks. Moreover, the system's exceptional performance in securing IoT devices, which are frequently targeted due to their ubiquity and vulnerabilities, highlights its potential as a reliable and scalable security measure. The FPGA-based IDS offers a significant contribution to the field by providing a rapid, accurate, and real-time security solution that addresses the pressing need for effective threat detection and prevention in modern communication networks.
Machine Learning , Network , Security , Data communication , Intrusion detection
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