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How to cite this paper
APA: 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.
Chicago/Turabian: 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, no. 1 (2025): 77-86.
AMA: Salloum, S., Gaber, T., Almaiah, M., Shehab, R., Al-Ali, R & Aldahyan, T. Adoption deep learning approach using realistic synthetic data for enhancing network intrusion detection in intelligent vehicle systems. International Journal of Data and Network Science. 2025;9(1):77-86.
References
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Salam, M. A., Azar, A. T., Elgendy, M. S., & Fouad, K. M. (2021). The effect of different dimensionality reduction techniques on machine learning overfitting problem. International Journal of Advanced Computer Science and Applications, 12(4), 641–655.
Scarfone, K., & Mell, P. (2007). Guide to intrusion detection and prevention systems (IDPS). NIST Special Publication, 800(94).
Schrötter, M., Niemann, A., & Schnor, B. (2024). A comparison of neural-network-based intrusion detection against signature-based detection in IoT networks. Information, 15(3), 164.
Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing & Management, 45(4), 427–437.
Sommer, R., & Paxson, V. (2010). Outside the closed world: On using machine learning for network intrusion detection. In 2010 IEEE Symposium on Security and Privacy (pp. 305–316).
Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009). A detailed analysis of the KDD CUP 99 data set. In 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications (pp. 1–6).
Tharwat, A. (2020). Classification assessment methods. Applied Computing and Informatics, 17(1), 168–192.
van Rijsbergen, C. J. (1979). Information retrieval. Butterworth-Heinemann.
Xiao, Y., Xing, C., Zhang, T., & Zhao, Z. (2019). An intrusion detection model based on feature reduction and convolutional neural networks. IEEE Access, 7, 42210–42219.
Yu, H., Wang, Z., Xie, Y., & Wang, G. (2024). A multi-granularity hierarchical network for long-and short-term forecasting on multivariate time series data. Applied Soft Computing, 111537.
Bahlali, A. R., & Bachir, A. (2023). Machine learning anomaly-based network intrusion detection: Experimental evaluation. In International Conference on Advanced Information Networking and Applications (pp. 392–403).
Bansal, S., & Bansal, N. (2015). Scapy—a Python tool for security testing. Journal of Computer Science & Systems Biology, 8(3), 140.
Biondi, P. (2010). Scapy documentation (!). 469, 155–203.
Buczak, A. L., & Guven, E. (2015). A survey of data mining and machine learning methods for cybersecurity intrusion detec-tion. IEEE Communications Surveys & Tutorials, 18(2), 1153–1176.
Ciric, V., Milosevic, M., Sokolovic, D., & Milentijevic, I. (2024). Modular deep learning-based network intrusion detection architecture for real-world cyber-attack simulation. Simulation Modelling Practice and Theory, 102916.
Clausen, H., Grov, G., & Aspinall, D. (2021). Cbam: A contextual model for network anomaly detection. Computers, 10(6), 79.
Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874.
Giacinto, G., Roli, F., & Didaci, L. (2003). Fusion of multiple classifiers for intrusion detection in computer networks. Pattern Recognition Letters, 24(12), 1795–1803.
Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning. MIT Press.
Hahn, D., Munir, A., & Behzadan, V. (2019). Security and privacy issues in intelligent transportation systems: Classification and challenges. IEEE Intelligent Transportation Systems Magazine, 13(1), 181–196.
Idrissi, M. J., et al. (2023). Fed-anids: Federated learning for anomaly-based network intrusion detection systems. Expert Sys-tems with Applications, 234, 121000.
Kruegel, C., & Toth, T. (2003). Using decision trees to improve signature-based intrusion detection. In International Work-shop on Recent Advances in Intrusion Detection (pp. 173–191).
Lazarevic, A., Kumar, V., & Srivastava, J. (2005). Intrusion detection: A survey. In Managing Cyber Threats: Issues, Ap-proaches, and Challenges (pp. 19–78).
Mehta, S., Paunwala, C., & Vaidya, B. (2019). CNN based traffic sign classification using Adam optimizer. In 2019 Interna-tional Conference on Intelligent Computing and Control Systems (ICCS) (pp. 1293–1298).
Moustafa, N., Keshk, M., Choo, K.-K. R., Lynar, T., Camtepe, S., & Whitty, M. (2021). DAD: A distributed anomaly detection system using ensemble one-class statistical learning in edge networks. Future Generation Computer Systems, 118, 240–251.
Murugan, P. (2018). Implementation of deep convolutional neural network in multi-class categorical image classification. arXiv Preprint arXiv1801.01397.
Nawaal, B., Haider, U., Khan, I. U., & Fayaz, M. (2024). Signature-based intrusion detection system for IoT. In Cyber Security for Next-Generation Computing Technologies (pp. 141–158). CRC Press.
Network Flows. (2024). Kaggle. https://www.kaggle.com/datasets/saidasalloum/network-flows.
Okoli, U. I., Obi, O. C., Adewusi, A. O., & Abrahams, T. O. (2024). Machine learning in cybersecurity: A review of threat de-tection and defense mechanisms. World Journal of Advanced Research and Reviews.
Openart AI. (2024). https://openart.ai/home.
Park, S., & Kwak, N. (2017). Analysis on the dropout effect in convolutional neural networks. In Computer Vision–ACCV 2016: 13th Asian Conference on Computer Vision, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part II 13 (pp. 189–204).
Phulre, A. K., Jain, S., & Jain, G. (2024). Evaluating security enhancement through machine learning approaches for anomaly-based intrusion detection systems. In 2024 IEEE International Students’ Conference on Electrical, Electronics and Com-puter Science (SCEECS) (pp. 1–5).
Powers, D. M. W. (2020). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correla-tion. arXiv Preprint arXiv2010.16061.
Ring, M., Wunderlich, S., Scheuring, D., Landes, D., & Hotho, A. (2019). A survey of network-based intrusion detection data sets. Computers & Security, 86, 147–167.
Salam, M. A., Azar, A. T., Elgendy, M. S., & Fouad, K. M. (2021). The effect of different dimensionality reduction techniques on machine learning overfitting problem. International Journal of Advanced Computer Science and Applications, 12(4), 641–655.
Scarfone, K., & Mell, P. (2007). Guide to intrusion detection and prevention systems (IDPS). NIST Special Publication, 800(94).
Schrötter, M., Niemann, A., & Schnor, B. (2024). A comparison of neural-network-based intrusion detection against signature-based detection in IoT networks. Information, 15(3), 164.
Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing & Management, 45(4), 427–437.
Sommer, R., & Paxson, V. (2010). Outside the closed world: On using machine learning for network intrusion detection. In 2010 IEEE Symposium on Security and Privacy (pp. 305–316).
Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009). A detailed analysis of the KDD CUP 99 data set. In 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications (pp. 1–6).
Tharwat, A. (2020). Classification assessment methods. Applied Computing and Informatics, 17(1), 168–192.
van Rijsbergen, C. J. (1979). Information retrieval. Butterworth-Heinemann.
Xiao, Y., Xing, C., Zhang, T., & Zhao, Z. (2019). An intrusion detection model based on feature reduction and convolutional neural networks. IEEE Access, 7, 42210–42219.
Yu, H., Wang, Z., Xie, Y., & Wang, G. (2024). A multi-granularity hierarchical network for long-and short-term forecasting on multivariate time series data. Applied Soft Computing, 111537.