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Bit-Level Automotive Controller Area Network Message Reverse Framework Based on Linear Regression.

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  • 1School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China.

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|February 15, 2022
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Summary
This summary is machine-generated.

This study introduces a new method to reverse-engineer Controller Area Network (CAN) messages, crucial for vehicle cybersecurity. The framework accurately reconstructs CAN message details, enhancing in-vehicle intrusion detection systems and testing.

Keywords:
Controller Area Networkbit-leveldatabase CANelectronic control unitsmultiple linear regressionreversevehicle behavior

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Area of Science:

  • Automotive Cybersecurity
  • Network Protocol Analysis
  • Reverse Engineering

Background:

  • Modern vehicles utilize Electronic Control Units (ECUs) connected via Controller Area Network (CAN) bus, creating cybersecurity vulnerabilities.
  • Malicious attacks often involve injecting harmful messages into the CAN network.
  • Current methods for reversing CAN messages lack completeness, hindering security research and development.

Purpose of the Study:

  • To develop a novel framework for bit-level inversion of CAN messages to approximate Database CAN (DBC) file information.
  • To improve the accuracy of CAN message analysis for cybersecurity applications.
  • To overcome limitations of existing CAN message reversal techniques.

Main Methods:

  • Utilizing multiple linear regression to model vehicle behavior and CAN traffic.
  • Implementing a framework that filters candidate messages using decision coefficients.
  • Determining data length and bit alignment by analyzing model parameters.

Main Results:

  • The proposed framework achieves high accuracy in reversing CAN messages.
  • The system demonstrates superior performance in boundary delineation compared to existing methods.
  • Effectively filters relevant messages from actual vehicle CAN traffic.

Conclusions:

  • The multiple linear regression-based framework offers a significant advancement in understanding CAN message structures.
  • This approach enhances the development of robust in-vehicle intrusion detection systems (IDS) and security testing.
  • Provides a more comprehensive alternative to confidential DBC files for security analysis.