Federated Learning-based Vehicle Trajectory Prediction against
  Cyberattacks
Zhe Wang Tingkai Yan
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.