An Effective Federated Learning Approach for Secure and Private Scalable Intrusion Detection on the Internet of Vehicles

Abstract

The rapid proliferation of connected vehicles in the Internet of Vehicles (IoV) has introduced significant data security and privacy challenges, emphasizing the need for advanced intrusion detection systems (IDS). This article proposes a federated learning-based intrusion detection system (FL-IDS), explicitly designed to identify both external network-level threats and internal vehicular cyberattacks. Federated learning enables collaborative training across distributed vehicles without sharing raw data, significantly reducing communication overhead and preserving data privacy. To further enhance privacy, differential privacy (DP) mechanisms are applied, ensuring sensitive information remains protected even during model updates. Additionally, secure communication channels are established using Secure Sockets Layer/Transport Layer Security (SSL/TLS) protocols, effectively safeguarding the integrity and authenticity of data exchanges between vehicles, roadside units, and cloud servers. Robust preprocessing methods, including data balancing, normalization, and feature selection, are combined with an adaptive federated learning strategy (FedXgbBagging) specifically designed to address the challenges posed by heterogeneous and non-independent and identically distributed (non-IID) data. Extensive evaluations on two real-world datasets, CSE-CIC-IDS2018 for network attacks and CICIoV2024 for in-vehicle Controller Area Network (CAN) bus attacks-show remarkable performance, achieving accuracy rates of 99.64% and 99.99%, respectively. The proposed FL-IDS significantly outperforms existing methods, demonstrating its robustness, adaptability, and scalability in securing IoV environments against diverse cyber threats.