Federated Learning-Driven Intrusion Detection Framework Using Edge Intelligence for Secure IoT and Vehicular Networks

Anwer Saleh Khamees Al-Shammari, Noor Esam Alyassiri, Sabrin Alsayyab, Mahdi Saleh and Saif Wali Ali Alsudani

References:

1. N. A. Hamad, K. A. A. Bakar, F. Qamar, A. M. Jubair, R. R. Mohamed, and M. A. Mohamed, “Systematic analysis of federated learning approaches for intrusion detection in the Internet of Things environment,” IEEE Access, vol. 13, pp. 95410–95444, 2025, doi: 10.1109/ACCESS.2025.3574672.
2. K. Begum, M. A. I. Mozumder, M.-I. Joo, and H.-C. Kim, “BFLIDS: Blockchain-driven federated learning for intrusion detection in IoMT networks,” Sensors, vol. 24, no. 14, p. 4591, 2024, doi: 10.3390/s24144591.
3. A. Alruwaili, S. Islam, and I. Gondal, “Fed-DTB: A dynamic trust-based framework for secure and efficient federated learning in IoV networks: Securing V2V/V2I communication,” Journal of Cybersecurity and Privacy, vol. 5, no. 3, p. 48, 2025, doi: 10.3390/jcp5030048.
4. M. Raza, M. J. Saeed, M. B. Riaz, and M. A. Sattar, “Federated learning for privacy-preserving intrusion detection in software-defined networks,” IEEE Access, vol. 12, pp. 69551–69567, 2024, doi: 10.1109/ACCESS.2024.3395997.
5. L. Lazaros, D. E. Koumadorakis, A. G. Vrahatis, and S. Kotsiantis, “Federated learning: Navigating the landscape of collaborative intelligence,” Electronics, vol. 13, no. 23, p. 4744, 2024, doi: 10.3390/electronics13234744.
6. S. Alsudani, H. Nasrawi, M. Shattawi, and A. Ghazikhani, “Enhancing spam detection: A crow-optimized FFNN with LSTM for email security,” WJCMS, vol. 3, no. 1, pp. 28–39, Mar. 2024, doi: 10.31185/wjcms.199.
7. E. Dritsas and M. Trigka, “Federated learning for IoT: A survey of techniques, challenges, and applications,” Journal of Sensor and Actuator Networks, vol. 14, no. 1, p. 9, 2025, doi: 10.3390/jsan14010009.
8. M. J. J. Rakkini, R. Mohanram, G. Dheepak, S. Subha, R. Hemalatha, and M. R. Suresh, “Transformer-based intrusion detection systems: A deep federated learning approach for privacy-preserving cybersecurity,” in Proc. 6th Int. Conf. Data Intelligence and Cognitive Informatics (ICDICI), Tirunelveli, India, 2025, pp. 216–223, doi: 10.1109/ICDICI66477.2025.
9. S. W. A. Alsudani and G. K. Saud, “Recurrent neural network optimized by grasshopper for accurate audio data-based diagnosis of Parkinson’s disease,” WJPS, vol. 4, no. 2, pp. 56–75, Jun. 2025, doi: 10.31185/wjps.766.
10. T. Al-Shurbaji, et al., “Deep learning-based intrusion detection system for detecting IoT botnet attacks: A review,” IEEE Access, vol. 13, pp. 11792–11822, 2025, doi: 10.1109/ACCESS.2025.3526711.
11. N. A. Al-Khulaidi, A. T. Zahary, A. A. Al-Shargabi, and M. A. S. Hazaa, “Machine learning for intrusion detection in vehicular ad-hoc networks (VANETs): A survey,” in Proc. 4th Int. Conf. Emerging Smart Technologies and Applications (eSmarTA), Sana’a, Yemen, 2024, pp. 1–10, doi: 10.1109/eSmarTA62850.2024.10639016.
12. N. Manogaran, Y. B. Shankar, M. Nandagopal, H.-K. Su, W.-K. Kuo, S. Ravichandran, and K. Seerangan, “Federated learning and EEL-Levy optimization in CPS ShieldNet fusion: A new paradigm for cyber–physical security,” Sensors, vol. 25, no. 12, p. 3617, 2025, doi: 10.3390/s25123617.
13. T. Basri, “Securing VANETs for Internet of Things (IoT): AI-driven solutions for privacy and intrusion detection,” in AI-Driven Transportation Systems: Real-Time Applications and Related Technologies, H. Maryam, M. M. Malik, I. U. Khan, and S. K. Gupta, Eds. Cham, Switzerland: Springer, 2025, vol. 62, pp. 113–132, doi: 10.1007/978-3-031-98349-8_6.
14. S. T. Banafshehvaragh, M. Zarei, and A. M. Rahmani, “A reliable score-based routing protocol using a fog-assisted intrusion detection system in vehicular ad-hoc networks,” Scientific Reports, vol. 15, p. 25709, 2025, doi: 10.1038/s41598-025-08228-3.
15. B. Buyuktanir, Ş. Altinkaya, G. Karatas Baydogmus, et al., “Federated learning in intrusion detection: Advancements, applications, and future directions,” Cluster Computing, vol. 28, p. 473, 2025, doi: 10.1007/s10586-025-05325-w.
16. A. Bhardwaj and S. Singh, “Machine learning-driven task offloading for smart vehicular edge computing: Taxonomy, issues, and opportunities,” in Convergence of AI, Federated Learning, and Blockchain for Sustainable Development, M. Kumar, A. Nayyar, A. K. Singh, and Y. Guo, Eds. Cham, Switzerland: Springer, 2025, pp. 145–164, doi: 10.1007/978-3-031-80949-1_9.
17. M. Alharthi, F. Medjek, and D. Djenouri, “Ensemble learning approaches for multi-class intrusion detection systems for the Internet of Vehicles (IoV): A comprehensive survey,” Future Internet, vol. 17, no. 7, p. 317, 2025, doi: 10.3390/fi17070317.
18. M. Shahin, A. Hosseinzadeh, and F. F. Chen, “A two-stage hybrid federated learning framework for privacy-preserving IoT anomaly detection and classification,” IoT, vol. 6, no. 3, p. 48, 2025, doi: 10.3390/iot6030048.
19. R. Abreu, E. Simão, C. Serôdio, F. Branco, and A. Valente, “Enhancing IoT security in vehicles: A comprehensive review of AI-driven solutions for cyber-threat detection,” AI, vol. 5, no. 4, pp. 2279–2299, 2024, doi: 10.3390/ai5040112.
20. H. G. A. Umar, I. Yasmeen, M. Aoun, et al., “Energy-efficient deep learning-based intrusion detection system for edge computing: A novel DNN-KDQ model,” Journal of Cloud Computing, vol. 14, p. 32, 2025, doi: 10.1186/s13677-025-00762-9.
21. A. Khraisat, M. A. Talukder, M. A. Uddin, et al., “RF-FedAvg: Federated learning-based random forest model for intrusion detection in wireless sensor networks,” Cluster Computing, vol. 28, p. 873, 2025, doi: 10.1007/s10586-025-05591-8.
22. R. Islam, S. Mazumdar, and R. Islam, “An experiment on feature selection using logistic regression,” in Proc. 5th Information Communication Technologies Conf. (ICTC), Nanjing, China, 2024, pp. 319–324, doi: 10.1109/ICTC61510.2024.10602330.
23. Q. Zeng, S. Olatunde-Salawu, and F. Nait-Abdesselam, “FGA-IDS: A federated learning and GAN-augmented intrusion detection system for UAV networks,” in Proc. IEEE 10th Int. Conf. Collaboration and Internet Computing (CIC), Washington, DC, USA, 2024, pp. 50–59, doi: 10.1109/CIC62241.2024.00017.
24. S. W. Nourildean, W. Mefteh, and A. M. Frihida, “Hybrid deep learning model-based intrusion detection system to improve artificial Internet of Things against cyber attacks,” in Advanced Information Networking and Applications (AINA 2025), Lecture Notes on Data Engineering and Communications Technologies, vol. 252, L. Barolli, Ed. Cham, Switzerland: Springer, 2025, pp. 1–10, doi: 10.1007/978-3-031-87784-1_25.
25. C. Thana-Aksaneekorn, S. Kosolsombat, and T. Luangwiriya, “Machine learning classification for intrusion detection systems using the NSL-KDD dataset,” in Proc. IEEE Int. Conf. Cybernetics and Innovations (ICCI), Chonburi, Thailand, 2024, pp. 1–6, doi: 10.1109/ICCI60780.2024.10532265.
26. S. Alshathri, A. Sayed, and E. E.-D. Hemdan, “An intelligent attack detection framework for the Internet of autonomous vehicles with imbalanced car hacking data,” World Electric Vehicle Journal, vol. 15, no. 8, p. 356, 2024, doi: 10.3390/wevj15080356.
27. O. Arreche, T. R. Guntur, J. W. Roberts, and M. Abdallah, “E-XAI: Evaluating black-box explainable AI frameworks for network intrusion detection,” IEEE Access, vol. 12, pp. 23954–23988, 2024, doi: 10.1109/ACCESS.2024.3365140.
28. M. H. Bhavsar, Y. B. Bekele, K. Roy, J. C. Kelly, and D. Limbrick, “FL-IDS: Federated learning-based intrusion detection system using edge devices for transportation IoT,” IEEE Access, vol. 12, pp. 52215–52226, 2024, doi: 10.1109/ACCESS.2024.3386631.
29. S. B. H. Samir, M. Raissa, H. Touati, et al., “Machine learning-based intrusion detection for securing in-vehicle CAN bus communication,” SN Computer Science, vol. 5, no. 1, p. 1082, 2024, doi: 10.1007/s42979-024-03465-1.
30. M. M. El-Gayar, F. A. F. Alrslani, and S. El-Sappagh, “Smart collaborative intrusion detection system for securing vehicular networks using ensemble machine learning model,” Information, vol. 15, no. 10, p. 583, 2024, doi: 10.3390/info15100583.
31. C. Xu, F. Zhang, Z. Yang, et al., “A few-shot network intrusion detection method based on mutual centralized learning,” Scientific Reports, vol. 15, p. 9848, 2025, doi: 10.1038/s41598-025-93185-0.
32. O. Ceviz, P. Sadioglu, S. Sen, and V. G. Vassilakis, “A novel federated learning-based IDS for enhancing UAVs privacy and security,” Internet of Things, vol. 31, p. 101592, 2025, doi: 10.1016/j.iot.2025.101592.