Privacy-Preserving Vehicle Intrusion Detection System Using Federated Learning and Homomorphic Encryption

DOI

10.2991/978-94-6239-638-8_31How to use a DOI?

Keywords

vehicle intrusion detection system; federated learning; homomorphic encryption; cybersecurity; data privacy; connected vehicles

Abstract

As vehicles become more connected and autonomous, intrusion detection systems must adapt to emerging threats while preserving user privacy. This paper presents a privacy-preserving vehicle IDS that integrates federated learning (FL) and homomorphic encryption (HE) to detect denial-of-service, fuzzy, and impersonation attacks on Controller Area Network (CAN) traffic. Experiments were conducted on a publicly available CAN Intrusion Dataset using three models: Decision Tree (DT), Support Vector Classifier (SVC), and K-Nearest Neighbors (KNN). FL enables decentralized model training without exposing raw data, while CKKS-based HE secures encrypted aggregation. Results show that the FL + HE system maintains high detection accuracy with reasonable runtime overhead, making it suitable for offline or near-real-time diagnostic applications in privacy-sensitive vehicular environments.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Chloe Soriano de Leon
AU  - Cedric Angelo Festin
PY  - 2026
DA  - 2026/04/30
TI  - Privacy-Preserving Vehicle Intrusion Detection System Using Federated Learning and Homomorphic Encryption
BT  - Proceedings of the  Workshop on Computation: Theory and Practice (WCTP 2025)
PB  - Atlantis Press
SP  - 594
EP  - 610
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6239-638-8_31
DO  - 10.2991/978-94-6239-638-8_31
ID  - deLeon2026
ER  -