Abstract-As the functions of vehicles are more computerized for the safety and convenience of drivers, attack surfaces of vehicle are accordingly increasing. Many attack results have shown that an attacker could intentionally control vehicles. Most of them exploit the vulnerability that controller area network (CAN) protocol, a de-facto standard for the in-vehicle networks, does not support message origin authentication. Although a number of methods to resolve this security vulnerability have been suggested, they have their each limitation to be applied into the current system. They have required either the modification of the CAN standard or dramatical communication load increase, which is infeasible in practice. In this paper, we propose a novel identification method, which works in the physical layer of the in-vehicle CAN network. Our method identifies electronic control units (ECUs) using inimitable characteristics of electrical CAN signals enabling detection of a malicious ECU. Unlike previous attempts to address the security problem in the in-vehicle CAN network, our method works by simply adding a monitoring unit to the existing network, making it deployable in current systems and compliant with required CAN standards. Our experimental results show that our method is able to correctly identify ECUs. In case of misclassfication rate for ECU idnetification, our method yields 0.36% in average which is approximate four times lower than the method proposed by P.-S. Murvay et al. This paper is also the first to identify potential attack models that systems should be able to detect.
Choi et al. REF proposed an electronic control units (ECU) identification method that resolves the fundamental security problem of in-vehicle CAN network, statistical features are extracted from the measured electrical CAN signal, three binary classifiers are used to detection of a malicious ECU.
Identifying ECUs Using Inimitable Characteristics of Signals in Controller Area Networks
IEEE Transactions on Vehicular Technology
IEEE Transactions on Vehicular Technology
Computer Science