Date of Award
Fall 2016
Project Type
Thesis
Program or Major
Electrical and Computer Engineering
Degree Name
Master of Science
First Advisor
Qiaoyan Yu
Second Advisor
Thomas Miller
Third Advisor
Franklin Rudolph
Abstract
Unlike the early automobiles that were mechanical and isolated, the modern automobile is a much more electronic and connected system. Connectivity to external networks, such as the Internet, is advantageous. However, it has also opened various attack points into the car’s In-Vehicle Network (IVN). The primary IVN used for communication is the Controller Area Network (CAN). The CAN was built in 1986 only for reliable real-time communication at the time. However, the need for security on CAN has become a serious concern with the advancement in automotive technology. In previous work, various types of attacks such as masquerade, replay, denial-of-service, and starvation attacks have been demonstrated to attack a CAN network. This thesis concentrates on low-cost detection techniques against masquerade and replay attacks on CAN. Using the invariables of CAN, we developed methods that achieved latency reduction of up to 68% initially and up to 3 orders in the later stages. Since hardware cost is also a concern, we propose methods that require only 1.8% and 6.2% of additional ROM and RAM, respectively as compared to a baseline CAN configuration. Our methods were also evaluated on the basis of their speed of response after attack detection. We were able to achieve response times of as low as 40μs. Lastly, we evaluated our method on the basis of attack misdetection rate and achieved the rates of the order of as low as 10^−5.
Recommended Citation
Ansari, Mohammad Raashid, "Low-Cost Approaches to Detect Masquerade and Replay Attacks on Automotive Controller Area Network" (2016). Master's Theses and Capstones. 868.
https://scholars.unh.edu/thesis/868