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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• 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. ‘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.},
      URL = {http://chess.eecs.berkeley.edu/pubs/1111.html}
  }
  

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