  Machine learning (ML) has made incredible impacts and transformations in a
wide range of vehicular applications. As the use of ML in Internet of Vehicles
(IoV) continues to advance, adversarial threats and their impact have become an
important subject of research worth exploring. In this paper, we focus on
Sybil-based adversarial threats against a deep reinforcement learning
(DRL)-assisted IoV framework and more specifically, DRL-based dynamic service
placement in IoV. We carry out an experimental study with real vehicle
trajectories to analyze the impact on service delay and resource congestion
under different attack scenarios for the DRL-based dynamic service placement
application. We further investigate the impact of the proportion of
Sybil-attacked vehicles in the network. The results demonstrate that the
performance is significantly affected by Sybil-based data poisoning attacks
when compared to adversary-free healthy network scenario.
