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Growing Science » International Journal of Data and Network Science » Adoption deep learning approach using realistic synthetic data for enhancing network intrusion detection in intelligent vehicle systems

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International Journal of Data and Network Science

ISSN 2561-8156 (Online) - ISSN 2561-8148 (Print)
Quarterly Publication
Volume 9 Issue 1 pp. 77-86 , 2025

Adoption deep learning approach using realistic synthetic data for enhancing network intrusion detection in intelligent vehicle systems Pages 77-86 Right click to download the paper Download PDF

Authors: Said A. Salloum, Tarek Gaber, Mohammed Amin Almaiah, Rami Shehab, Romel Al-Ali, Theyazan H.H Aldahyan

DOI: 10.5267/j.ijdns.2024.10.001

Keywords: Convolutional Neural Network (CNN), Cybersecurity, Intelligent Vehicle Systems, Network Intrusion Detection Scapy, Network Traffic Analysis, Simulation, Threat Detection

Abstract: In the dynamic field of cybersecurity within intelligent vehicle systems, the sophistication of threats necessitates continual advancements in security technologies. Traditional Network Intrusion Detection Systems (NIDS) often fall short in detecting emerging and sophisticated intrusion methods, primarily due to their reliance on static datasets that fail to capture the nuanced dynamics and complexity of modern network intrusions. This study presents a sophisticated simulation for NIDS tailored to intelligent vehicle environments, utilizing the extensive capabilities of Scapy—a robust network manipulation tool—to generate a highly accurate dataset of network traffic reflective of real-world scenarios. We created a diverse dataset involving 100,000 network flows, covering a wide array of benign, malicious, and anomalous traffic patterns, to thoroughly evaluate the detection capabilities of our proposed system. This dataset was analyzed using a deep learning framework employing a Convolutional Neural Network (CNN), which demonstrated outstanding performance metrics: an accuracy of 99.08%, precision of 98.96%, recall of 99.11%, and an F1 score of 99.03%. These metrics showcase the system's enhanced capability to precisely classify various network flows, emphasizing the importance of realistic synthetic data in boosting the training and accuracy of NIDS in intelligent vehicles. The results of this research are significant, marking a step forward towards more flexible and preemptive security measures for intelligent vehicles, and effectively narrowing the gap between simulation-based testing and real-world network environments.



How to cite this paper
Salloum, S., Gaber, T., Almaiah, M., Shehab, R., Al-Ali, R & Aldahyan, T. (2025). Adoption deep learning approach using realistic synthetic data for enhancing network intrusion detection in intelligent vehicle systems.International Journal of Data and Network Science, 9(1), 77-86.

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Journal: International Journal of Data and Network Science | Year: 2025 | Volume: 9 | Issue: 1 | Views: 403 | Reviews: 0

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