A Federated LSTM Autoencoder Framework for Privacy-Preserving Intrusion Detection in V2X Networks
Received: 2 July 2025 | Revised: 8 August 2025, 3 September 2025, and 7 September 2025 | Accepted: 9 September 2025 | Online: 9 February 2026
Corresponding author: Β. Vishwanath
Abstract
The fast growth of Vehicle-to-Everything (V2X) networks requires privacy-preserving Intrusion Detection Systems (IDSs) for effective operation. The proposed Federated Long Short-Term Memory Autoencoder (Fed-LSTM-AE) framework allows distributed vehicular clients to perform collaborative anomaly detection through model parameter sharing without exchanging raw data. The framework enables each client to create its own LSTM-based autoencoder model of normal traffic patterns while sharing only model parameters with a central server through federated learning to maintain data privacy and improve system scalability. Experiments using the VeReMi dataset show that Fed-LSTM-AE achieves better performance than the centralized LSTM, one-dimensional Convolutional Neural Network (1D CNN), Random Forest, and Isolation Forest baseline methods in terms of detection accuracy, F1-score, and Area Under the Receiver Operating Characteristic Curve (ROC-AUC) metrics. The framework shows strong detection performance against various attack types while achieving efficient federated training convergence and maintaining stability under non-Independent and Identically Distributed (non-IID) data conditions. The results demonstrate Fed-LSTM-AE's suitability for real-world V2X deployments because it maintains privacy protection while being adaptable and communication-efficient.
Keywords:
V2X security, federated learning, LSTM autoencoder, vehicular networks, privacy preservation, anomaly detectionDownloads
References
W. A. Mansouri, S. Asklany, S. H. Othman, and A. A. Darem, "New Scheduling Scheme in Cellular V2X Communication," Engineering, Technology & Applied Science Research, vol. 14, no. 3, pp. 14096–14101, June 2024. DOI: https://doi.org/10.48084/etasr.7275
Z. Hussain, A. ur R. Khan, H. Mehdi, and S. M. A. Saleem, "Analysis of Device-to-Device Communication over Double-Generalized Gamma Channels," Engineering, Technology & Applied Science Research, vol. 8, no. 4, pp. 3265–3269, Aug. 2018. DOI: https://doi.org/10.48084/etasr.2230
D. R. K. Raja, Z. A. Abas, C. S. Akula, Y. D. Kumar, G. H. Kumar, and V. Eswari, "Artificial intelligence powered internet of vehicles: securing connected vehicles in 6G," Indonesian Journal of Electrical Engineering and Computer Science, vol. 35, no. 1, pp. 213–221, July 2024. DOI: https://doi.org/10.11591/ijeecs.v35.i1.pp213-221
D. Wang, A. Qiu, Q. Zhou, and H. D. Schotten, "A Survey on the Role of Artificial Intelligence and Machine Learning in 6G-V2X Applications." arXiv, June 12, 2025. DOI: https://doi.org/10.1109/WF-IoT64238.2025.11270555
G. H. Kumar, D. R. Kumar Raja, H. D. Varun, Navyashree, Abhishek, and S. Nandikol, "Optimizing Spatial Efficiency Through Velocity-Responsive Controller in Vehicle Platooning," in 2024 8th International Conference on Computational System and Information Technology for Sustainable Solutions, Bengaluru, India, 2024, pp. 1–5. DOI: https://doi.org/10.1109/CSITSS64042.2024.10816902
E. Farsimadan, L. Moradi, and F. Palmieri, "A Review on Security Challenges in V2X Communications Technology for VANETs," IEEE Access, vol. 13, pp. 31069–31094, 2025. DOI: https://doi.org/10.1109/ACCESS.2025.3541035
H. Alabdouli, M. S. Hassan, and A. Abdelfatah, "Enhancing Route Guidance with Integrated V2X Communication and Transportation Systems: A Review," Smart Cities, vol. 8, no. 1, Feb. 2025, Art. no. 24. DOI: https://doi.org/10.3390/smartcities8010024
R. Sedar, C. Kalalas, F. Vázquez-Gallego, L. Alonso, and J. Alonso-Zarate, "A Comprehensive Survey of V2X Cybersecurity Mechanisms and Future Research Paths," IEEE Open Journal of the Communications Society, vol. 4, pp. 325–391, 2023. DOI: https://doi.org/10.1109/OJCOMS.2023.3239115
Z. Pethő, T. M. Kazár, Z. Szalay, and Á. Török, "Quantifying Cyber Risks: The Impact of DoS Attacks on Vehicle Safety in V2X Networks," IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 11, pp. 18591–18600, Nov. 2024. DOI: https://doi.org/10.1109/TITS.2024.3436840
M. S. Abood et al., "An LSTM-Based Network Slicing Classification Future Predictive Framework for Optimized Resource Allocation in C-V2X," IEEE Access, vol. 11, pp. 129300–129310, 2023. DOI: https://doi.org/10.1109/ACCESS.2023.3332225
A. R. Abdellah, A. Muthanna, M. H. Essai, and A. Koucheryavy, "Deep Learning for Predicting Traffic in V2X Networks," Applied Sciences, vol. 12, no. 19, Oct. 2022, Art. no. 10030. DOI: https://doi.org/10.3390/app121910030
F. Khanmohammadi and R. Azmi, "Time-Series Anomaly Detection in Automated Vehicles Using D-CNN-LSTM Autoencoder," IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 8, pp. 9296–9307, Aug. 2024. DOI: https://doi.org/10.1109/TITS.2024.3380263
C. Xu, H. Wu, Y. Zhang, S. Dai, H. Liu, and J. Tian, "A Real-Time Complex Road AI Perception Based on 5G-V2X for Smart City Security," Wireless Communications and Mobile Computing, vol. 2022, no. 1, Jan. 2022, Art. no. 4405242. DOI: https://doi.org/10.1155/2022/4405242
"VeReMi dataset." Github.io. [Online]. Available: https://veremi-dataset.github.io/.
V. Elangovan, W. Xiang, and S. Liu, "A Real-Time C-V2X Beamforming Selector Based on Effective Sequence to Sequence Prediction Model Using Transitional Matrix Hard Attention," IEEE Access, vol. 11, pp. 10954–10965, 2023. DOI: https://doi.org/10.1109/ACCESS.2023.3241130
S. Hossain, S.-M. Senouci, B. Brik, and A. Boualouache, "A privacy-preserving Self-Supervised Learning-based intrusion detection system for 5G-V2X networks," Ad Hoc Networks, vol. 166, Jan. 2025, Art. no. 103674. DOI: https://doi.org/10.1016/j.adhoc.2024.103674
S. Lee, K. Koufos, C. Maple, and M. Dianati, "Application of Unsupervised Learning in Implementation of Joint Power and Index Modulation Access in V2X Systems," IEEE Transactions on Cognitive Communications and Networking, vol. 9, no. 5, pp. 1308–1321, Oct. 2023. DOI: https://doi.org/10.1109/TCCN.2023.3276992
A. Gupta and X. Fernando, "Federated Reinforcement Learning for Collaborative Intelligence in UAV-Assisted C-V2X Communications," Drones, vol. 8, no. 7, July 2024, Art. no. 321. DOI: https://doi.org/10.3390/drones8070321
Y. T. Gebrezgiher, S. R. Jeremiah, S. Gritzalis, and J. H. Park, "VAE-Based Real-Time Anomaly Detection Approach for Enhanced V2X Communication Security," Applied Sciences, vol. 15, no. 12, June 2025, Art. no. 6739. DOI: https://doi.org/10.3390/app15126739
I.-S. Na, A. Haldorai, and N. Naik, "Federal Deep Learning Approach of Intrusion Detection System for In-Vehicle Communication Network Security," IEEE Access, vol. 13, pp. 2215–2228, 2025. DOI: https://doi.org/10.1109/ACCESS.2024.3521661
S. K. Kwon, J. H. Seo, J. Y. Yun, and K.-D. Kim, "Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication," Applied Sciences, vol. 11, no. 21, Nov. 2021, Art. no. 10420. DOI: https://doi.org/10.3390/app112110420
S. A. Abdel Hakeem and H. Kim, "Advancing Intrusion Detection in V2X Networks: A Comprehensive Survey on Machine Learning, Federated Learning, and Edge AI for V2X Security," IEEE Transactions on Intelligent Transportation Systems, vol. 26, no. 8, pp. 11137–11205, Aug. 2025. DOI: https://doi.org/10.1109/TITS.2025.3558849
M. Almehdhar et al., "Deep Learning in the Fast Lane: A Survey on Advanced Intrusion Detection Systems for Intelligent Vehicle Networks," IEEE Open Journal of Vehicular Technology, vol. 5, pp. 869–906, 2024. DOI: https://doi.org/10.1109/OJVT.2024.3422253
W. Aljabri, Md. A. Hamid, and R. Mosli, "Lightweight and Adaptive Data-Driven Intrusion Detection System for Autonomous Vehicles," IEEE Transactions on Intelligent Transportation Systems, vol. 26, no. 2, pp. 2282–2292, Feb. 2025. DOI: https://doi.org/10.1109/TITS.2024.3509459
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, July 2025, Art. no. 317. DOI: https://doi.org/10.3390/fi17070317
Downloads
How to Cite
License
Copyright (c) 2025 Β. Vishwanath, Chandrasekhar P. Reddy
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
