Available at: https://digitalcommons.calpoly.edu/theses/1810
Date of Award
MS in Computer Science
Security is a necessity when dealing with new forms of technology that may not have been analyzed from a security perspective. One of the latest growing technological advances are Vehicular Ad-Hoc Networks (VANETs). VANETs allow vehicles to communicate information to each other wirelessly which allows for an increase in safety and efficiency for vehicles. However, with this new type of computerized system comes the need to maintain security on top of it.
In order to try to protect location privacy of the vehicles in the system, vehicles change pseudonyms or identifiers at areas known as mix zones. This thesis implements a model that characterizes the attack surface of an adversarial insider vehicle inside of a VANET. This adversarial vehicle model describes the interactions and effects that an attacker vehicle can have on mix zones in order to lower the overall location privacy of the system and remain undetected to defenders in the network. In order to reach the final simulation of the model, several underlying models had to be developed around the interactions of defender and attacker vehicles.
The evaluation of this model shows that there are significant impacts that internal attacker vehicles can have on location privacy within mix zones. From the created simulations, the results show that having one to five optimal attackers shows a decrease of 0.6%-2.6% on the location privacy of the network and a 12% decrease in potential location privacy in a mix zone where an attacker defects in a 50-node network. The industry needs to consider implementing defenses based on this particular attack surface discussed.
Available for download on Tuesday, March 19, 2019