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KR-102962464-B1 - METHOD AND DEVICE FOR IDENTIFYING ID-SPECIFIC FUNCTIONS OF VEHICLE COMMUNICATION PROTOCOL

KR102962464B1KR 102962464 B1KR102962464 B1KR 102962464B1KR-102962464-B1

Abstract

A method for identifying functions by ID of a vehicle communication protocol performed by an electronic device is disclosed. The method for identifying functions by ID includes the steps of classifying collected vehicle communication protocol data by ID, generating a signal by ID based on the vehicle communication protocol data classified by ID, extracting features by ID based on the vehicle communication protocol data classified by ID and the signal by ID, and predicting functions corresponding to each ID included in the vehicle communication protocol data based on the extracted features by ID.

Inventors

  • 전상훈
  • 박상민

Assignees

  • 국민대학교산학협력단

Dates

Publication Date
20260507
Application Date
20231025

Claims (20)

  1. In a method for identifying functions by ID of a vehicle communication protocol performed by an electronic device, A step of classifying vehicle communication protocol data, including multiple messages transmitted and received using a vehicle communication protocol, according to an ID included in each of the multiple messages; A step of generating an ID-specific signal based on vehicle communication protocol data classified by the above ID; A step of extracting ID-specific features based on vehicle communication protocol data classified by ID and the ID-specific signals; and The method includes the step of predicting vehicle functions corresponding to each ID included in the vehicle communication protocol data based on the extracted ID-specific features; The features for each ID above are, A method for identifying functions by ID, including the dynamic time warping (DTW) distance between different ID signals.
  2. In Article 1, The above vehicle communication protocol data is, A method for identifying functions by ID, comprising at least one of CAN (Controller Area Network) data and CAN-FD (CAN with Flexible Data rate) data.
  3. In Article 1, The above vehicle communication protocol data is, A method for identifying functions by ID, comprising at least one of vehicle communication protocol data generated in a stationary vehicle and vehicle communication protocol data generated in a driving vehicle.
  4. In Article 1, The step of generating the above ID-specific signal is, A method for identifying functions by ID, comprising the step of extracting a signal by ID in units of a preset time interval from vehicle communication protocol data classified by ID.
  5. In Article 1, The features for each ID above are, A method for identifying functions by ID, comprising at least one of a bit flip rate by ID and a bit histogram by ID.
  6. In Article 5, The step of extracting features by the above ID is, A step of extracting at least one of the ID-specific bit flip rate and the ID-specific bit histogram from the vehicle communication protocol data classified by the ID; and A method for identifying functions by ID, comprising the step of extracting the DTW distance between the different ID signals based on the above ID-specific signals.
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  8. In Article 1, The step of predicting functions corresponding to each of the above IDs is, A method for identifying functions by ID, comprising the step of inputting the extracted ID-specific features into an artificial intelligence model trained to predict functions corresponding to IDs.
  9. In Article 8, The above artificial intelligence model is, A method for identifying functions by ID, which is a model learned based on vehicle communication protocol data collected from the same vehicle as the vehicle in which the above vehicle communication protocol data is collected.
  10. In Article 8, The above artificial intelligence model is, A method for identifying functions by ID, which is a model learned based on vehicle communication protocol data collected from a vehicle other than the vehicle in which the above vehicle communication protocol data is collected.
  11. In Article 1, A method for identifying features by ID, comprising the step of training an artificial intelligence model based on the extracted features by ID.
  12. In Article 11, The step of training the above artificial intelligence model is, Based on the extracted ID-specific features and ID-specific function information, the artificial intelligence model is trained to predict the function corresponding to the ID, and The above function information by ID is, A method for identifying functions by ID, obtained from a vehicle communication protocol database containing information about functions corresponding to IDs.
  13. In electronic devices, A memory for storing vehicle communication protocol data including a plurality of messages transmitted and received using a vehicle communication protocol; and A processor comprising: classifying the vehicle communication protocol data according to an ID included in each of the plurality of messages; generating an ID-specific signal based on the vehicle communication protocol data classified by ID; extracting ID-specific features based on the vehicle communication protocol data classified by ID and the ID-specific signal; and predicting vehicle functions corresponding to each ID included in the vehicle communication protocol data based on the extracted ID-specific features. The features for each ID above are, An electronic device including the dynamic time warping (DTW) distance between different ID signals.
  14. In Article 13, The above processor is, An electronic device that extracts a signal for each ID in units of a preset time interval from vehicle communication protocol data classified by the above ID.
  15. In Article 13, The features for each ID above are, An electronic device comprising at least one of a bit flip rate per ID and a bit histogram per ID.
  16. In Article 15, The above processor is, An electronic device that extracts at least one of the ID-specific bit flip rate and the ID-specific bit histogram from vehicle communication protocol data classified by the ID, and extracts the DTW distance between the different ID signals based on the ID-specific signals.
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  18. In Article 13, The above memory is, Store an artificial intelligence model trained to predict a function corresponding to an ID, and The above processor is, An electronic device that inputs the extracted ID-specific features into the artificial intelligence model to predict functions corresponding to each ID included in the vehicle communication protocol data.
  19. In Article 13, The above processor is, An electronic device that trains an artificial intelligence model based on the extracted ID-specific features.
  20. In a non-transient computer-readable recording medium storing computer instructions that cause said electronic device to perform an operation when executed by a processor of said electronic device, The above operation is, A step of classifying vehicle communication protocol data, including multiple messages transmitted and received using a vehicle communication protocol, according to an ID included in each of the multiple messages; A step of generating an ID-specific signal based on vehicle communication protocol data classified by the above ID; A step of extracting ID-specific features based on vehicle communication protocol data classified by ID and the ID-specific signals; and The method includes the step of predicting vehicle functions corresponding to each ID included in the vehicle communication protocol data based on the extracted ID-specific features; The features for each ID above are, A recording medium including the dynamic time warping (DTW) distance between different ID signals.

Description

Method and device for identifying ID-specific functions of a vehicle communication protocol The present invention relates to an electronic device and a method for identifying functions by ID of a vehicle communication protocol performed by the electronic device. Most in-vehicle cybersecurity issues involve the exploitation of vehicle communication protocols such as CAN (Controller Area Network) or CAN-FD (CAN with Flexible Data rate). To properly defend against cyber attacks on vehicles, it is necessary to know the functions corresponding to the IDs included in the messages of vehicle communication protocols. However, the functions assigned to each ID in vehicle communication protocols are not standardized due to security issues, and which ID is responsible for which vehicle function may vary depending on the vehicle manufacturer, or even within the same manufacturer, depending on the vehicle model or year. Due to the characteristics of these vehicle communication protocols, it is difficult to identify which function an ID is responsible for in the target vehicle, which imposes limitations on the analysis of vehicle communication protocol data. For example, even if only the desired function is executed in the vehicle to collect data regarding a specific function, the collected data includes not only the data associated with the ID responsible for that function but also basic or periodic data generated by the vehicle. In other words, since the collected data includes data from various IDs other than the one intended for collection, it is difficult to analyze the data for the specific function being analyzed. Meanwhile, although there is a publicly available database that analyzes and provides the functions assigned by vehicle communication protocol IDs for each vehicle, such databases contain information only for specific vehicles and do not cover all function-specific IDs for every vehicle. Furthermore, there are cases where incorrect function information is analyzed that differs from the actual function assigned by the vehicle communication protocol ID. Furthermore, the existing analysis of vehicle communication protocol IDs by assigned function was performed in a heuristic manner, which is time-consuming and often results in analysis based on incorrect information. FIG. 1 is a block diagram illustrating a CAN (Controller Area Network) communication system according to one embodiment of the present invention. FIG. 2 is a drawing for explaining a CAN message format according to an embodiment of the present invention. FIG. 3 is a block diagram of an electronic device according to one embodiment of the present invention. FIG. 4 is a diagram illustrating the configuration and operation of a processor according to one embodiment of the present invention. FIG. 5 is a diagram illustrating training data according to an embodiment of the present invention. FIG. 6 is a diagram illustrating training data according to an embodiment of the present invention. FIG. 7 is a block diagram of an electronic device according to one embodiment of the present invention. FIG. 8 is a flowchart illustrating the operation method of an electronic device according to one embodiment of the present invention. FIG. 9 is a flowchart illustrating the operation method of an electronic device according to one embodiment of the present invention. FIG. 10 is a flowchart illustrating the operation method of an electronic device according to one embodiment of the present invention. The various embodiments of the present invention described below with reference to the drawings are not intended to limit the scope to specific embodiments and should be understood to include various modifications, equivalents, and/or alternatives. In relation to the description of the drawings, similar reference numerals may be used for similar components. In describing the present invention, detailed descriptions of related prior art are omitted if it is determined that such descriptions would unnecessarily obscure the essence of the present disclosure. Additionally, redundant descriptions of identical components are to be omitted whenever possible. The suffix "bu" for components used in the following description is assigned or used interchangeably solely for the ease of drafting the specification, and does not inherently possess a distinct meaning or role. The terms used in this invention are for the purpose of describing embodiments and are not intended to limit or/or restrict the invention. The singular expression includes the plural expression unless the context clearly indicates otherwise. In the present invention, terms such as 'comprising' or 'having' are intended to specify the existence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not excluding in advance the existence or addition of one or more other features, numbers, steps, actions, components, parts, or c