Increasing connectivity and automation in vehicles leads to a greater potential attack surface. Such vulnerabilities within vehicles can also be used for auto-theft, increasing the potential for attackers to disable anti-theft mechanisms implemented by vehicle manufacturers. We utilize patterns derived from Controller Area Network (CAN) bus traffic to verify driver “behavior”, as a basis to prevent vehicle theft. Our proposed model uses semi-supervised learning that continuously profiles a driver, using features extracted from CAN bus traffic. We have selected 15 key features and obtained an accuracy of 99% using a dataset comprising a total of 51 features across 10 different drivers. We use a number of data analysis algorithms, such as J48, Random Forest, JRip and clustering, using 94K records. Our results show that J48 is the best performing algorithm in terms of training and testing (1.95 seconds and 0.44 seconds recorded, respectively). We also analyze the effect of using a sliding window on algorithm performance, altering the size of the window to identify the impact on prediction accuracy.
ATVSA: Vehicle Driver Profiling for Situational Awareness
01.06.2022
995781 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Enhancing driver situational awareness through crowd intelligence
Tema Archiv | 2015
|Vehicle Situational Awareness Technologies
SAE Technical Papers | 2008
|Vehicle Situational Awareness Technologies
British Library Conference Proceedings | 2008
|