CAN-D: A Modular Four-Step Pipeline for Comprehensively Decoding Controller Area Network Data

TL;DR

CAN-D is a modular pipeline that accurately decodes vehicle CAN data by identifying signal boundaries, endianness, and signedness, enabling comprehensive real-time vehicle information extraction across different makes and models.

Contribution

The paper introduces CAN-D, the first comprehensive, modular solution capable of decoding any CAN signal by addressing endianness and signedness, with state-of-the-art accuracy and real-time hardware implementation.

Findings

CAN-D achieves 5x lower average error than previous methods.

Novel classifiers for signal boundaries outperform prior approaches.

Signedness classification accuracy exceeds 97% F-score.

Abstract

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, impeding vehicle-related research. Guided by the 4-part CAN signal definition, we present CAN-D (CAN-Decoder), a modular, 4-step pipeline for identifying each signal's boundaries (start bit, length), endianness (byte order), signedness (bit-to-integer encoding), and by leveraging diagnostic standards, augmenting a subset of the extracted signals with physical interpretation. We provide a comprehensive review of the CAN signal reverse engineering research. 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. 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. We provide two novel, state-of-the-art signal boundary classifiers-both 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. CAN-D is the only solution with the potential to extract any CAN signal. In evaluation on 10 vehicles, CAN-D's average $\ell^1$ error is 5x better than all previous methods and exhibits lower ave. error, even when considering only signals that meet prior methods' assumptions. CAN-D is implemented in lightweight hardware, allowing for an OBD-II plugin for real-time in-vehicle CAN decoding.