Modern vehicles have evolved into supporting advanced internal networks and connecting System Based Chips (SBC), System in a Package (SiP) solutions or traditional micro controllers to foster an electronic ecosystem for high speed data transfers, precision and real-time control. The use of Controller Area Networks (CAN) is widely adopted as the backbone of internal vehicle communication infrastructure. Automotive applications such as ADAS, autonomous driving, battery management systems, power train systems, telematics and infotainment, all utilize CAN transmissions directly or through gateway management. The network transmissions lack robust integrity verification mechanisms to validate authentic data payloads, making it vulnerable to packet replay, spoofing, insertion, deletion and denial of service attacks. Additional methods exist to secure network data such as traditional cryptography. Utilizing this method will increase the computational complexity, processing latency and increase overall system cost. This thesis proposes a robust, light and adaptive solution to validate the authenticity of automotive sensor data using CAN network protocol. We propose using a two-dimensional Quantization Index Modulation (QIM) data hiding technique, to create a means of verification. Analysis of the proposed framework will be conducted in a sensor transmission scenario for RADAR sensors in an autonomous vehicle setting. The detection and effects of distortion on the application are tested through the implementation of sensor fusion algorithms and the results are observed and analyzed. The proposed framework offers a needed capability to maintain transmission integrity without the compromise of data quality and low design complexity. This framework could also be applied to different network architectures, as well as its operational scope could be modified to operate with more abstract types of data.MSEElectrical Engineering, College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/167354/1/Brandon Fedoruk - Final Thesis.pd
