The Internet of Vehicles (IoV) refers to a network where vehicles are connected to each other and to the surrounding infrastructure, enabling the exchange of information among vehicles and with the infrastructure to improve safety, efficiency, and overall transportation experience. However, intrusion into IoV systems may compromise the privacy of vehicle owners and occupants, with unauthorized access to personal data collected by vehicles leading to privacy violations and identity theft. Anomaly-based Network Intrusion Detection System (A-NIDS) provides a means to recognize prospective threats by detecting deviations from the normal model. This work introduces an intrusion detection system for vehicles using Autoencoder and Decoder for Image-Based detection, offering automated operation without supervision. © 2024 Elsevier B.V., All rights reserved.