Investigating Algorithms for Automotive Keyless Entry Intrusion Detection

Automotive keyless entry systems use wireless communication to communicate information between the key and the car using Bluetooth technology. Such communication is susceptible to security risks including intrusion, where a malicious user injects signals into the system to cause a malfunction potentially resulting in unauthorized access. Thus, it is important to investigate methods that can be used to detect such an intrusion, in order to avoid any risks that may arise due to a potential malfunction.
The proposed research aims to investigate methods that can be used for intrusion detection. Hella will provide the data sets necessary for this work, and the research team at UBC will apply signal-processing methods for feature extraction, and then use the extracted features along with statistical or machine learning methods to detect an abnormality in the data, which may hint to an intrusion.

Intern: 
Connor Gaudreau
Faculty Supervisor: 
Anas Chaaban
Province: 
British Columbia
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