  The automation and connectivity of CAV inherit most of the cyber-physical
vulnerabilities of incumbent technologies such as evolving network
architectures, wireless communications, and AI-based automation. This book
chapter entails the cyber-physical vulnerabilities and risks that originated in
IT, OT, and the physical domains of the CAV ecosystem, eclectic threat
landscapes, and threat intelligence. To deal with the security threats in
high-speed, high dimensional, multimodal data and assets from eccentric
stakeholders of the CAV ecosystem, this chapter presents and analyzes some of
the 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 have
proposed, trained, and tested the deep CNN-LSTM architecture for CAV threat
intelligence; assessed and compared the performance of the proposed model
against other deep learning algorithms such as DNN, CNN, LSTM. Our results
indicate the superiority of the proposed model although DNN and 1d-CNN also
achieved more than 99% of accuracy, precision, recall, f1-score, and AUC on the
CAV-KDD dataset. The good performance of deep CNN-LSTM comes with the increased
model complexity and cumbersome hyperparameters tuning. Still, there are open
challenges on deep learning adoption in the CAV cybersecurity paradigm due to
lack of properly developed protocols and policies, poorly defined privileges
between stakeholders, costlier training, adversarial threats to the model, and
poor generalizability of the model under out of data distributions.
