Federated Learning-based Vehicle Trajectory Prediction against Cyberattacks
This addresses cybersecurity challenges in vehicle communication to prevent collisions and data leaks, but it appears incremental as it builds on existing federated learning and cyberattack defense methods.
The paper tackles the problem of cybersecurity in the Internet of Vehicles by proposing a federated learning-based algorithm for vehicle trajectory prediction against cyberattacks, achieving improvements of up to 6.99% in cyberattack detection and 54.86% in trajectory prediction under maximum attack scenarios compared to benchmarks.
With the development of the Internet of Vehicles (IoV), vehicle wireless communication poses serious cybersecurity challenges. Faulty information, such as fake vehicle positions and speeds sent by surrounding vehicles, could cause vehicle collisions, traffic jams, and even casualties. Additionally, private vehicle data leakages, such as vehicle trajectory and user account information, may damage user property and security. Therefore, achieving a cyberattack-defense scheme in the IoV system with faulty data saturation is necessary. This paper proposes a Federated Learning-based Vehicle Trajectory Prediction Algorithm against Cyberattacks (FL-TP) to address the above problems. The FL-TP is intensively trained and tested using a publicly available Vehicular Reference Misbehavior (VeReMi) dataset with five types of cyberattacks: constant, constant offset, random, random offset, and eventual stop. The results show that the proposed FL-TP algorithm can improve cyberattack detection and trajectory prediction by up to 6.99% and 54.86%, respectively, under the maximum cyberattack permeability scenarios compared with benchmark methods.