A comprehensive intrusion detection method for the internet of vehicles based on federated learning architecture

Abstract

Cybersecurity breaches within the Internet of Vehicles (IoV) have been increasingly reported annually with the proliferation of intelligent connected vehicles. Two primary obstacles are faced by current intrusion detection systems: substantial computational demands and stringent data privacy regulations, complicating both efficient deployment and the safeguarding of data privacy. Consequently, there is a pressing need for intrusion detection solutions that are both efficient and considerate of privacy concerns. This paper introduces FED-IoV, an innovative intrusion detection method tailored for the IoV, leveraging a federated learning architecture. FED-IoV aims to collaboratively perform detection tasks across distributed edge devices, thereby minimizing data privacy risks. Vehicular communication traffic data is transformed into images, and a bespoke, efficient model, MobileNet-Tiny, is employed for feature extraction, rendering FED-IoV capable of achieving high detection accuracy whilst being viable for deployment on devices with limited resources. Through evaluation against the authoritative datasets CAN-Intrusion and CICIDS2017, exceptional accuracy rates of 98.51 % and 97.74 %, respectively, were demonstrated by FED-IoV within a federated learning context, and excellent detection capabilities on imbalanced datasets were also shown. Moreover, a prediction latency of under 10 milliseconds per sample was maintained on devices with limited computational power, such as the Raspberry Pi 4 8GB, showcasing significantly better accuracy and real-time performance relative to existing approaches. The successful deployment of FED-IoV ushers in a novel, privacy-preserving, and efficient intrusion detection solution for IoV security.