Hybrid Threat Detection Models for Cybersecurity in Autonomous Vehicle Networks
Keywords:
Cyber-attacksAbstract
For the first time in the academic literature, the contributions made in this paper were designed and implemented for our proposed hierarchical anomaly detection model for EVs "ABLE" and "NADINE" [1]. These two algorithmic models have been integrated to perform as feature aware supervised anomaly detection models predicting vehicle type, wireless network performance and attack impact cluster under the hierarchical abnormal behaviour classification. The results demonstrated an increased detection accuracy and specificity compared to a single shot model. The performance improvement of both the models is supported by being able to handle data in a more insightful manner when making feature-aware decisions in detections.
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