ByCAN：从位级到字节级逆向工程控制器局域网（CAN）报文
ByCAN: Reverse Engineering Controller Area Network (CAN) Messages from Bit to Byte Level
AI总结：
针对CAN协议解码规范为OEM专有黑盒的问题，提出全自动逆向工程系统ByCAN，通过字节级聚类与位/字节多特征融合，无需先验知识即可解码CAN帧，在真实CAN帧分析中实现了较高的切片与标注性能。
摘要：
作为现代汽车的主要标准协议，控制器局域网（Controller Area Network，CAN）是汽车网络安全威胁及自动驾驶应用的关键研究对象。由于CAN的解码规范是由原始设备制造商（Original Equipment Manufacturers，OEMs）维护的专有黑盒，在无法全面理解CAN报文含义的情况下，开展相关研究和行业开发会面临挑战。本文提出了一种名为ByCAN的全自动逆向工程系统，用于对CAN报文进行逆向工程。ByCAN引入字节级聚类，并整合字节和位两个层级的多种特征，性能优于现有研究。ByCAN采用聚类和模板匹配算法，无需先验知识即可自动解码CAN帧的规范。实验结果表明，ByCAN在切片和标注性能（即CAN信号边界与标签的识别）上达到了高精度。在实验中，ByCAN分析真实世界CAN帧时，切片准确率达80.21%，切片覆盖率达95.21%，通用标签的标注准确率达68.72%。
英文摘要：
As the primary standard protocol for modern cars, the Controller Area Network (CAN) is a critical research target for automotive cybersecurity threats and autonomous applications. As the decoding specification of CAN is a proprietary black-box maintained by Original Equipment Manufacturers (OEMs), conducting related research and industry developments can be challenging without a comprehensive understanding of the meaning of CAN messages. In this paper, we propose a fully automated reverse-engineering system, named ByCAN, to reverse engineer CAN messages. ByCAN outperforms existing research by introducing byte-level clusters and integrating multiple features at both byte and bit levels. ByCAN employs the clustering and template matching algorithms to automatically decode the specifications of CAN frames without the need for prior knowledge. Experimental results demonstrate that ByCAN achieves high accuracy in slicing and labeling performance, i.e., the identification of CAN signal boundaries and labels. In the experiments, ByCAN achieves slicing accuracy of 80.21%, slicing coverage of 95.21%, and labeling accuracy of 68.72% for general labels when analyzing the real-world CAN frames.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
