通过控制器局域网的物理指纹将接收到的数据包链接到发送器

Omid Avatefipour, Azeem Hafeez, M. Tayyab, Hafiz Malik
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引用次数: 39

摘要

控制器局域网(CAN)总线作为车载数据通信的传统协议。简单、健壮和适合于实时系统是CAN总线协议的显著特点。然而,它缺乏基本的安全特性,如按摩身份验证,这使得它容易受到欺骗攻击。在CAN网络中,将CAN数据包连接到发送节点是一项具有挑战性的任务。本文旨在通过开发一个框架来将每个CAN数据包链接到其源来解决这个问题。接收包的物理信号属性由通道和节点(或设备)组成,其中包含特定的唯一工件,被认为是为了实现这一目标。物理通道和数字设备中的材料和设计缺陷是设备通道特定独特工件背后的主要影响因素,它们被用来将接收到的电信号连接到发射器。一般来说,来自每个ecu的不可模仿的信号模式在一段时间内存在,这可以体现所提出方法的稳定性。在时域和频域研究了信道器件特定属性的唯一性。特征向量由时域和频域物理属性组成,然后用于训练基于神经网络的分类器。通过使用从16个不同通道和4个相同ecu传输相同消息收集的数据集来评估所提出的指纹识别方法的性能。实验结果表明,该方法对通道和ECU分类的正确率分别达到95.2%和98.3%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Linking received packet to the transmitter through physical-fingerprinting of controller area network
The Controller Area Network (CAN) bus serves as a legacy protocol for in-vehicle data communication. Simplicity, robustness, and suitability for real-time systems are the salient 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, which are the main contributing factors behind the device-channel specific unique artifacts, are leveraged to link the received electrical signal to the transmitter. Generally, the inimitable patterns of signals from each ECUs exist over the course of time that can manifest the stability of the proposed method. Uniqueness of the channel-device specific attributes are also investigated for time-and frequency-domain. Feature vector is made up of both time and frequency domain physical attributes and then employed to train a neural network-based classifier. Performance of the proposed fingerprinting method is evaluated by using a dataset collected from 16 different 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.
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