AI-Driven Intrusion Detection Framework for Securing Industrial Internet of Things Environments
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This report documents the complete project lifecycle, spanning theoretical foundations, system analysis and design, implementation details, and empirical evaluation. The outcomes confirm that artificial intelligence techniques can significantly strengthen the security posture of IIoT deployments, offering a scalable and adaptive defense mechanism against evolving cyber threats.
This work was conducted at Arab International University (AIU), Syria. The official website of the university is: https://www.aiu.edu.sy
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Alishmso_AIU_Applied_Project_Report.pdf
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References
- [1] T. Barhoum, 'SETF: A Structured Engineering Thesis Framework for Artificial Intelligence, Software Engineering, and Robotics', Zenodo, Apr. 2026. doi: 10.5281/zenodo.19686845.
- [2] R. Vinayakumar, M. Alazab, K. P. Soman, P. Poornachandran, A. Al-Nemrat, and S. Venkatraman, "Deep learning approach for intelligent intrusion detection system," IEEE Access, vol. 7, pp. 41525–41550, 2019.
- [3] Y. Zhang, L. Chen, and J. Wang, "Random Forest-based intrusion detection for smart grid networks with optimized feature selection," IEEE Trans. Smart Grid, vol. 11, no. 4, pp. 3128–3140, 2020.
- [4] M. A. Khan, S. Abbas, and A. Rehman, "Hybrid CNN-LSTM model for network anomaly detection in IoT environments," Comput. Secur., vol. 108, p. 102345, 2021.
- [5] Q. Li, Z. He, and H. Gao, "Federated learning for privacy-preserving intrusion detection in distributed IIoT networks," IEEE Internet Things J., vol. 9, no. 18, pp. 17842–17855, 2022.
- [6] I. Alrashdi, A. Alqazzaz, E. Aloufi, and R. Alharthi, "Lightweight intrusion detection for resource-constrained IoT edge devices," Sensors, vol. 23, no. 5, p. 2641, 2023.
- [7] I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, "Toward generating a new intrusion detection dataset and intrusion traffic characterization," in Proc. 4th Int. Conf. Inf. Syst. Secur. Privacy (ICISSP), 2018, pp. 108–116.
- [8] N. Moustafa and J. Slay, "UNSW-NB15: A comprehensive data set for network intrusion detection systems," in Proc. Military Commun. Inf. Syst. Conf. (MilCIS), 2015, pp. 1–6.
- [9] A. L. Buczak and E. Guven, "A survey of data mining and machine learning methods for cyber security intrusion detection," IEEE Commun. Surveys Tuts., vol. 18, no. 2, pp. 1153–1176, 2016.
- [10] M. A. Al-Garadi, A. Mohamed, A. K. Al-Ali, X. Du, I. Ali, and M. Guizani, "A survey of machine and deep learning methods for Internet of Things (IoT) security," IEEE Commun. Surveys Tuts., vol. 22, no. 3, pp. 1646–1685, 2020.