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The automation and connectivity of CAV inherit most of the cyber-physicalvulnerabilities of incumbent technologies such as evolving networkarchitectures, wireless communications, and AI-based automation. This bookchapter entails the cyber-physical vulnerabilities and risks that originated inIT, OT, and the physical domains of the CAV ecosystem, eclectic threatlandscapes, and threat intelligence. To deal with the security threats inhigh-speed, high dimensional, multimodal data and assets from eccentricstakeholders of the CAV ecosystem, this chapter presents and analyzes some ofthe state of art deep learning-based threat intelligence for attack detection.The frontiers in deep learning, namely Meta-Learning and Federated Learning,along with their challenges have been included in the chapter. We haveproposed, trained, and tested the deep CNN-LSTM architecture for CAV threatintelligence; assessed and compared the performance of the proposed modelagainst other deep learning algorithms such as DNN, CNN, LSTM. Our resultsindicate the superiority of the proposed model although DNN and 1d-CNN alsoachieved more than 99% of accuracy, precision, recall, f1-score, and AUC on theCAV-KDD dataset. The good performance of deep CNN-LSTM comes with the increasedmodel complexity and cumbersome hyperparameters tuning. Still, there are openchallenges on deep learning adoption in the CAV cybersecurity paradigm due tolack of properly developed protocols and policies, poorly defined privilegesbetween stakeholders, costlier training, adversarial threats to the model, andpoor generalizability of the model under out of data distributions.

A Deep Learning Perspective on Connected Automated Vehicle (CAV) Cybersecurity and Threat Intelligence