Car Hacking Dataset

Car hacking datasets are collections of data used to develop and evaluate security systems for vehicles, focusing on detecting and mitigating cyberattacks targeting various vehicle systems. Current research emphasizes developing robust anomaly detection models, often employing deep learning architectures like autoencoders, convolutional neural networks, and graph convolutional networks, to identify intrusions within CAN bus communications and predict vehicle trajectories. These datasets and the resulting models are crucial for improving the security and safety of autonomous and connected vehicles, contributing significantly to the development of more resilient and trustworthy automotive systems.