.png){kind=icon}
返回
CAN-D: A Modular Four-Step Pipeline for Comprehensively Decoding Controller Area Network Data
.png){kind=icon}
OA
AI
DOI：10.1109/TVT.2021.3092354.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Controller area networks (CANs) are a broadcast protocol for real-time communication of critical vehicle subsystems. Original equipment manufacturers of passenger vehicles hold secret their mappings of CAN data to vehicle signals, and these definitions vary according to make, model, and year. Without these mappings, the wealth of real-time vehicle information hidden in the CAN packets is uninterpretable, severely impeding vehicle-related research, including CAN cybersecurity and privacy studies, aftermarket tuning, efficiency and performance monitoring, and fault diagnosis to name a few. Guided by the four-part CAN signal definition, we present CAN-D (CAN-Decoder), a modular, four-step pipeline for identifying each signal's boundaries (start bit and length), endianness (byte ordering), signedness (bit-to-integer encoding), and by leveraging diagnostic standards, augmenting a subset of the extracted signals with meaningful, physical interpretation. En route to CAN-D, we provide a comprehensive review of the CAN signal reverse engineering research. All previous methods ignore endianness and signedness, rendering them incapable of decoding many standard CAN signal definitions. Incorporating endianness grows the search space from 128 to 4.72E21 signal tokenizations and introduces a web of changing dependencies. In response, we formulate, formally analyze, and provide an efficient solution to an optimization problem, allowing identification of the optimal set of signal boundaries and byte orderings. In addition, we provide two novel, state-of-the-art signal boundary classifiers-both of which are superior to previous approaches in precision and recall in three different test scenarios-and the first signedness classification algorithm, which exhibits a >97% F-score. Overall, CAN-D is the only solution with the potential to extract any CAN signal that is also the state of the art. In evaluation on 10 vehicles of different makes, CAN-D's average l(1) error is five times better (81% less) than all previous methods and exhibits lower average error, even when considering only signals that meet prior methods' assumptions. Finally, CAN-D is implemented in lightweight hardware, allowing for an on-board diagnostic (OBD-II) plugin for real-time in-vehicle CAN decoding.
Keyword:
Encoding
Reverse engineering
Engines
Decoding
Standards
Real-time systems
Pipelines
Controller area network (CAN)
reverse engineering
machine learning
security
privacy
technology
期刊
IF:
7.1
论文数:
1.8W
被引数:
6.6W
机构
引用论文
Identifying ECUs Using Inimitable Characteristics of Signals in Controller Area Networks使用控制器区域网络中信号的独特特征识别ecu
OAAI
OAAI分享收藏分享收藏CANet: An Unsupervised Intrusion Detection System for High Dimensional CAN Bus DataCANet: 一种面向高维CAN总线数据的无监督入侵检测系统.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容