Systematic Literature Review: Anomaly Detection in Connected and Autonomous Vehicles

This systematic literature review provides a structured and detailed overview of research on anomaly detection for connected and autonomous vehicles, focusing on the Artificial Intelligence methods employed, training approaches, and testing and evaluation techniques. The initial database search identified 2,160 articles, of which 203 were included in this review after rigorous screening and assessment. This study revealed that the most commonly used anomaly detection techniques employed are deep learning networks such as LSTM, CNN, and autoencoders, alongside one-class SVM. Most detection models were trained using real-world operational vehicle data, although anomalies, such as attacks and faults, were often injected artificially into the datasets. The models were evaluated primarily using five key evaluation metrics: recall, accuracy, precision, F1-score, and false positive rate. The most frequently used set of evaluation metrics for detection models were accuracy, precision, recall, and F1-score. The review makes several recommendations to improve future work related to anomaly detection models. It recommends providing comprehensive assessment of the anomaly detection models and emphasise the importance to share models publicly to facilitate collaboration within the research community and enable further validation. Recommendations also include the need for benchmarking datasets with predefined anomalies or cyberattacks (with comprehensive threat modelling) to test and improve the effectiveness of the proposed anomaly detection models. Future research should focus on the deployment of anomaly based detection in vehicles to evaluate their performance in real-world driving conditions, and explore systems using communication protocols beyond CAN, such as Ethernet and FlexRay.