CANMatch: A Fully Automated Tool for CAN Bus Reverse Engineering Based on Frame Matching

Buscemi, Alessio; Turcanu, Ion; Castignani, German; Crunelle, Romain; Engel, Thomas

doi:10.1109/tvt.2021.3124550

Cited by 13 publications

(6 citation statements)

References 24 publications

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“…Many current CAN decoding methods, such as LibreCAN [13] and CAN-D [14], use signalindependent features such as bit flipping rates to identify and decode signals, and then rely on manual work and correlation with labeled ground truth signals to label the decoded signals. While there has been previous work that leverages known decodings to further decode CAN signals [15], there has been no corresponding work to automate the labeling of these signals. Our solution, CANClassify, leverages known encodings and labels to further accelerate both decoding as well as labeling.…”

Section: Discussionmentioning

confidence: 99%

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CANClassify: Automated Decoding and Labeling of CAN Bus Signals

Ngo¹,

Sprinkle²,

Bhadani³

2022

J. Engg. Res. & Sci.

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Controller Area Network (CAN) bus data is used on most vehicles today to report and communicate sensor data. However, this data is generally encoded and is not directly interpretable by simply viewing the raw data on the bus. However, it is possible to decode CAN bus data and reverse engineer the encodings by leveraging knowledge about how signals are encoded and using independently recorded ground-truth signal values for correlation. While methods exist to support the decoding of possible signals, these methods often require additional manual work to label the function of each signal. In this paper, we present CANClassify -a method that takes in raw CAN bus data, and automatically decodes and labels CAN bus signals, using a novel convolutional interpretation method to preprocess CAN messages. We evaluate CANClassify's performance on a previously undecoded vehicle and confirm the encodings manually. We demonstrate performance comparable to the state of the art while also providing automated labeling. Examples and code are available at https://github.com/ngopaul/CANClassify.

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Section: Discussionmentioning

confidence: 99%

“…Our work leverages known encodings in order to further assist in the decoding of new CAN vehicles. In this way, it is similar to the work presented in CANMatch [15]. CANMatch leverages frame matching, which relies upon vehicle manufacturers' re-use of the same Message IDs and signal encodings across multiple makes and models.…”

Section: Can Signal Decodingmentioning

confidence: 98%

CANClassify: Automated Decoding and Labeling of CAN Bus Signals

Ngo¹,

Sprinkle²,

Bhadani³

2022

J. Engg. Res. & Sci.

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“…Currently, most in-car communication protocols depend on the wiring harness, such as CAN, FlexRay, and MOST. 5 , 6 It is noted that the wiring harness is the third heaviest component in a car and comprises 50% of the labor cost for the entire car. 5 Considering the necessity of replacement and the requirement of latency and reliability in smart cars, 7 SparkLink aims at not only replacing wired communications but also achieving performance in terms of latency and reliability comparable to wired communications.…”

Section: Typical Scenarios and Applications Of Sparklinkmentioning

confidence: 99%

SparkLink: A short-range wireless communication protocol with ultra-low latency and ultra-high reliability

et al. 2023

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“…Recently, it was demonstrated that fully automated reverse engineering can be achieved by finding a correspondence between the frames of a target vehicle and the ground truth of previously reverse engineered vehicles. The match can be found based on the ID of frames (if it has been preserved in the target vehicle [10]), or through ML fingerprinting (if the ID has been changed/anonymized [11]).…”

Section: Background and Related Workmentioning

confidence: 99%

“…In one of our recent studies [10], we propose an approach for fully automated CAN bus engineering, which, differently from related work, is context-agnostic, i.e., it does not require any ground truth about the data collection scenario. This solution is based on matching the frames of the target vehicle with the frames of a set of vehicles whose CAN specifications are already known (e.g.…”

Section: Introductionmentioning

confidence: 99%

Preventing Frame Fingerprinting in Controller Area Network Through Traffic Mutation

Buscemi

Turcanu

Castignani

et al. 2022

2022 IEEE International Conference on Communications Workshops (ICC Workshops)

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The continuous increase of connectivity in commercial vehicles is leading to a higher number of remote access points to the Controller Area Network (CAN) -the most popular in-vehicle network system. This factor, coupled with the absence of encryption in the communication protocol, poses serious threats to the security of the CAN bus. Recently, it has been demonstrated that CAN data can be reverse engineered via frame fingerprinting, i.e., identification of frames based on statistical traffic analysis. Such a methodology allows fully remote decoding of in-vehicle data and paves the way for remote precompiled vehicle-agnostic attacks. In this work, we propose a first solution against CAN frame fingerprinting based on mutating the traffic without applying modifications to the CAN protocol. The results show that the proposed methodology halves the accuracy of CAN frame fingerprinting.

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CANMatch: A Fully Automated Tool for CAN Bus Reverse Engineering Based on Frame Matching

Cited by 13 publications

References 24 publications

CANClassify: Automated Decoding and Labeling of CAN Bus Signals

CANClassify: Automated Decoding and Labeling of CAN Bus Signals

SparkLink: A short-range wireless communication protocol with ultra-low latency and ultra-high reliability

Preventing Frame Fingerprinting in Controller Area Network Through Traffic Mutation

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