Anti-counterfeiting Method of CAN Terminal Based on Device Physical Fingerprint

Xiaozhong Hu, A. Hu, Jiabao Yu, Yanjun Ding, Hongxing Hu, Peng Guo
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引用次数: 1

Abstract

With the development of science and technology, the electronic control unit (ECU) equipment has gradually become complex, and the security of the controller area network (CAN) bus protocol as the standard protocol for ECU communication has also been higher. In recent years, researchers have conducted extensive research on the security of the CAN bus, such as the anti-counterfeiting attack mechanism based on intrusion detection technology and the authentication code technology, which has improved the security of the CAN bus. However, directly modifying the CAN protocol itself based on intrusion detection technology will cause insufficient backward compatibility and a large number of devices cannot be reused; The authentication code-based approach cannot trace the origin of counterfeit devices and forensic evidence of attack devices, so further improvements are needed. In view of the above two problems, this paper proposes a CAN terminal anti-counterfeiting attack method based on physical fingerprint, which calculates the autocorrelation difference between the collected original signal and the calculated reconstructed signal, and obtains the physical fingerprint of the device as the basis for CAN terminal identity authentication, thereby achieving anti-counterfeiting attack. In this paper, 30 CAN terminals are used to experimentally verify the method in this paper, collect and transmit random data and transfer fixed data CAN frames to form two sets of data sets, and train and test using linear discriminant models, and the final model can identify the device identity rate on the two data sets up to 99%.
基于设备物理指纹的CAN终端防伪方法
随着科学技术的发展,电子控制单元(ECU)设备逐渐复杂化,作为ECU通信标准协议的控制器局域网(CAN)总线协议的安全性也越来越高。近年来,研究人员对CAN总线的安全性进行了广泛的研究,如基于入侵检测技术的防伪攻击机制和认证码技术,提高了CAN总线的安全性。但是,基于入侵检测技术直接修改CAN协议本身会导致向后兼容性不足,导致大量设备无法重用;基于身份验证码的方法无法追踪假冒设备的来源和攻击设备的取证证据,因此需要进一步改进。针对以上两个问题,本文提出了一种基于物理指纹的CAN终端防伪攻击方法,通过计算采集到的原始信号与计算得到的重构信号之间的自相关差,得到设备的物理指纹作为CAN终端身份认证的基础,从而实现防伪攻击。本文利用30台CAN终端对本文方法进行实验验证,采集和传输随机数据和传输固定数据CAN帧形成两组数据集,并使用线性判别模型进行训练和测试,最终模型在两组数据集上识别设备识别率高达99%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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