Physical-Fingerprinting of Electronic Control Unit (ECU) Based on Machine Learning Algorithm for In-Vehicle Network Communication Protocol ;;CAN-BUS”

In-vehicle network communication;CAN-Bus protocol;CAN-Bus Security;ECU fingerprinting;Artificial neural network;Machine learning algorithms;Computer Engineering;Computer Engineering, College of Engineering and Computer Science

Avatefipour, OmidWei, Lu ;

University of Michigan

关键词: In-vehicle network communication;    CAN-Bus protocol;    CAN-Bus Security;    ECU fingerprinting;    Artificial neural network;    Machine learning algorithms;    Computer Engineering;    Computer Engineering, College of Engineering and Computer Science;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/140731/Thesis%20manuscript_v3.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

TheControllerAreaNetwork(CAN)busservesasalegacyprotocolforin-vehicledata communication.Simplicity,robustness,andsuitabilityforreal-timesystemsarethesalient features of the CAN bus protocol. However, it lacks the basic security features such as massage authentication, which makes it vulnerable to the spoofing attacks. In a CAN network, linking CAN packet to the sender node is a challenging task. This paper aims to address this issue by developing a framework to link each CAN packet to its source. Physical signal attributes of the received packet consisting of channel and node (or device) which contains specific unique artifacts are considered to achieve this goal. Material and design imperfections in the physical channel and digital device, whicharethemaincontributingfactorsbehindthedevice-channelspecificuniqueartifacts,are leveraged to link the received electrical signal to the transmitter. Generally, the inimitable patterns ofsignalsfromeachECUsexistoverthecourseoftimethatcanmanifestthestabilityofthe proposed method.Uniqueness of the channel-device specific attributes are also investigated for time-andfrequency-domain.Featurevectorismadeupofbothtimeandfrequencydomain physical attributes and then employed to train a neural network-based classifier. Performance of theproposedfingerprintingmethodisevaluatedbyusingadatasetcollectedfrom16different channels and four identical ECUs transmitting same message. Experimental results indicate that the proposed method achieves correct detection rates of 95.2% and 98.3% for channel and ECU classification, respectively.