  Future Connected and Automated Vehicles (CAV), and more generally ITS, will
form a highly interconnected system. Such a paradigm is referred to as the
Internet of Vehicles (herein Internet of CAVs) and is a prerequisite to
orchestrate traffic flows in cities. For optimal decision making and
supervision, traffic centres will have access to suitably anonymized CAV
mobility information. Safe and secure operations will then be contingent on
early detection of anomalies. In this paper, a novel unsupervised learning
model based on deep autoencoder is proposed to detect the self-reported
location anomaly in CAVs, using vehicle locations and the Received Signal
Strength Indicator (RSSI) as features. Quantitative experiments on simulation
datasets show that the proposed approach is effective and robust in detecting
self-reported location anomalies.
