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ReCAN - Dataset for reverse engineering of Controller Area Networks.

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Area of Science:

  • Automotive Engineering
  • Cybersecurity
  • Data Science

Background:

  • Controller Area Network (CAN) buses are critical for vehicle communication.
  • Existing automotive security relies heavily on obscurity, which may be insufficient.
  • Publicly accessible vehicle data can enhance research and development.

Purpose of the Study:

  • To detail the methodology for extracting and decoding CAN bus data from vehicles.
  • To provide hardware and software requirements for data acquisition and processing.
  • To make code snippets publicly available for research reproducibility.

Main Methods:

  • Extraction of 36 million data frames from two personal vehicles and three commercial trucks.
  • Decoding raw CAN bus data frames into understandable sensor information.
  • Documentation of hardware and software prerequisites for the entire process.

Main Results:

  • A comprehensive dataset comprising raw and decoded CAN bus data was generated.
  • The study successfully extracted and decoded sensor data from multiple vehicle types.
  • The findings highlight the vulnerability of automotive data to interception using consumer-grade technology.

Conclusions:

  • The security-through-obscurity paradigm in automotive manufacturing is challenged by accessible data extraction methods.
  • Open-source code and accessible data facilitate further research into vehicle cybersecurity.
  • Understanding vehicle data accessibility is crucial for developing robust security measures.