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Recognizing Manipulated Electronic Control Units
Armin Wasicek, Weimerskirch Andre

Citation
Armin Wasicek, Weimerskirch Andre. "Recognizing Manipulated Electronic Control Units". SAE 2015 World Congress & Exhibition, April, 2015.

Abstract
Combatting the modification of automotive control systems is a current and future challenge for OEMs and suppliers. ‘Chip- tuning’ is a manifestation of manipulation of a vehicle’s original setup and calibration. With the increase in automotive functions implemented in software and corresponding business models, chip tuning will become a major concern. Recognizing tuned control units in a vehicle is required to report that circumstance for technical as well as legal reasons. This work approaches the problem by capturing the behavior of relevant control units within a machine learning system called a recognition module. The recognition module continuously monitors vehicle’s sensor data. It comprises a set of classifiers that have been trained on the intended behavior of a control unit before the vehicle is delivered. When the vehicle is on the road, the recognition module uses the classifier together with current data to ascertain that the behavior of the vehicle is as intended. A proof-of-concept implementation uses the TORCS racing simulator to generate traces of the engine’s behavior. The recognition module extracts features from these traces and feeds them to an artificial neural network (ANN). After training on different tracks, the ANN successfully distinguishes traces originating from the original racing car as well as traces taken from modified racing cars. The results show that assessing a vehicle’s behavior is feasible and contributes to protect its integrity against modifications. Additionally, the availability of a vehicle’s behavioral model can trigger even more interesting applications.

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Citation formats  
  • HTML
    Armin Wasicek, Weimerskirch Andre. <a
    href="http://chess.eecs.berkeley.edu/pubs/1111.html"
    >Recognizing Manipulated Electronic Control
    Units</a>, SAE 2015 World Congress & Exhibition,
    April, 2015.
  • Plain text
    Armin Wasicek, Weimerskirch Andre. "Recognizing
    Manipulated Electronic Control Units". SAE 2015 World
    Congress & Exhibition, April, 2015.
  • BibTeX
    @inproceedings{WasicekAndre15_RecognizingManipulatedElectronicControlUnits,
        author = {Armin Wasicek and Weimerskirch Andre},
        title = {Recognizing Manipulated Electronic Control Units},
        booktitle = {SAE 2015 World Congress \& Exhibition},
        month = {April},
        year = {2015},
        abstract = {Combatting the modification of automotive control
                  systems is a current and future challenge for OEMs
                  and suppliers. âChip- tuningâ is a
                  manifestation of manipulation of a vehicleâs
                  original setup and calibration. With the increase
                  in automotive functions implemented in software
                  and corresponding business models, chip tuning
                  will become a major concern. Recognizing tuned
                  control units in a vehicle is required to report
                  that circumstance for technical as well as legal
                  reasons. This work approaches the problem by
                  capturing the behavior of relevant control units
                  within a machine learning system called a
                  recognition module. The recognition module
                  continuously monitors vehicleâs sensor data. It
                  comprises a set of classifiers that have been
                  trained on the intended behavior of a control unit
                  before the vehicle is delivered. When the vehicle
                  is on the road, the recognition module uses the
                  classifier together with current data to ascertain
                  that the behavior of the vehicle is as intended. A
                  proof-of-concept implementation uses the TORCS
                  racing simulator to generate traces of the
                  engineâs behavior. The recognition module
                  extracts features from these traces and feeds them
                  to an artificial neural network (ANN). After
                  training on different tracks, the ANN successfully
                  distinguishes traces originating from the original
                  racing car as well as traces taken from modified
                  racing cars. The results show that assessing a
                  vehicleâs behavior is feasible and contributes
                  to protect its integrity against modifications.
                  Additionally, the availability of a vehicleâs
                  behavioral model can trigger even more interesting
                  applications.},
        URL = {http://chess.eecs.berkeley.edu/pubs/1111.html}
    }
    

Posted by Armin Wasicek on 10 Sep 2015.
Groups: chess
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