This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Recognizing Manipulated Electronic Control Units
Technical Paper
2015-01-0202
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Language:
English
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 and reporting of tuned control units in a vehicle is required 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 vehicles as well as traces taken from modified vehicles.
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.
Recommended Content
Topic
Citation
Wasicek, A. and Weimerskirch, A., "Recognizing Manipulated Electronic Control Units," SAE Technical Paper 2015-01-0202, 2015, https://doi.org/10.4271/2015-01-0202.Also In
References
- Kankar , P.K. , Sharma , S.C. , Harsha , S.P. Fault Diagnosis of Ball Bearings using Machine Learning Methods Elsevier Expert Systems with Applications 38 1876 1886 2011
- Wallentowitz , H. , Freialdenhoven , A. , Olschewski , I. Strategien in der Automobilindustrie: Technologietrends und Marktentwicklungen Teubner Verlag / GWV Fachverlage GmbH 2009 10.1007/978-3-8348-9311-6
- Wasicek , A. Copy protection for automotive electronic control units using authenticity heartbeat signals 10 th IEEE International Conference on Industrial Informatics (INDIN) 2012 10.1109/INDIN.2012.6301060
- Wasicek , A , El-Salloum , C. , Kopetz , H. Authentication in Time-Triggered Systems Using Time-Delayed Release of Keys 14th IEEE International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing (ISORC) 31 39 2011 10.1109/ISORC.2011.14
- Broy , M. , Kruger , I.H. , Pretschner , A. , Salzmann , C. Engineering Automotive Software Proceedings of the IEEE 95 2 356 373 Feb. 2007 10.1109/JPROC.2006.888386
- SAE Surface Vehicle Standard E/E Diagnostic Test Modes SAE Standard J1979 Sep. 2010
- Skorobogatov . Sergei P. . Copy Protection in Modern Microcontrollers http://www.cl.cam.ac.uk/sps32/mculock.html
- Wymann B. , Espi E.é , Guionneau C. , Dimitrakakis C. , Coulom R. , Sumner A. . TORCS: The Open Racing Car Simulator 2014
- Nanopoulos , A. , Alcock , R. , Manolopoulos , Y. Feature-based Classification of Time-series Data International Journal of Computer Research 10 49 61 2001
- Wu , J-D. , Liu , C.H. An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network Expert Systems with Applications 36 3 4278 4286 April 2009 10.1016/j.eswa.2008.03.008
- Chandola , V. , Banerjee , A. , Kumar , V. Anomaly Detection : A Survey ACM Computing Surveys 41 3 July 2009 10.1145/1541880.1541882
- Manevitz , L. , Yousef , M. One-class document classification via Neural Networks Elsevier, Neurocomputing 70 7 9 1466 1481 2007 10.1016/j.neucom.2006.05.013
- Hawkins , S. , He , H. , Williams , G. , Baxter , R. Outlier Detection Using Replicator Neural Networks Springer LCNS 2454, Data Warehousing and Knowledge Discovery 170 180 2002
- Markou , M. , Singh , S. Novelty detection: a review-part 2: neural network based approaches Elsevier, Signal Processing 83 12 2499 2521 2003 10.1016/j.sigpro.2003.07.019
- Wasicek , A. Protection of Intellectual Property Rights in Automotive Control Units SAE Int. J. Passeng. Cars - Electron. Electr. Syst. 7 1 201 212 2014 10.4271/2014-01-0338
- Hoppe , T. , Kiltz , S. , Dittmann , J. Security threats to automotive CAN networks - practical examples and selected short-term countermeasures Proceedings of the 27th international conference on Computer Safety, Reliability, and Security (SAFECOMP) 2008 235 248 10.1007/978-3-540-87698-4_21
- Studnia , I. , Nicomette , V. , Alata , E. , Deswarte , Y. , Kaâniche , M. , Laarouchi , Y. Survey on Security Threats and Protection Mechanisms in Embedded Automotive Networks The 2nd Workshop on Open Resilient human-aware Cyber-physical Systems (WORCS-2013) 2013
- Checkoway , S. , McCoy , D. , Kantor , B. , Anderson , D. , Shacham , H. , Savage , S. , Koscher , K. , Czeskis , A. , Roesner , F. , Kohno , T. et al. Comprehensive experimental analyses of automotive attack surfaces Proc. 20 th USENIX Security San Francisco, CA 2011
- Muter , M. , Asaj , N. Entropy-based anomaly detection for in-vehicle networks Porc. of IEEE Intelligent Vehicles Symposium (IV) 1110 1115 2011 10.1109/IVS.2011.5940552
- Petsche , T. , Marcantonio , A. , Darken , C. , Hanson , S.J. , Kuhn , G.M. , Santoso , I. A Neural Network Autoassociator for Induction Motor Failure Prediction NIPS MIT Press 924 930 1996