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Detection and Mitigation of Sensor and CAN Bus Attacks in Vehicle Anti-Lock Braking Systems

Published: 06 January 2022 Publication History

Abstract

For a modern vehicle, if the sensor in a vehicle anti-lock braking system (ABS) or controller area network (CAN) bus is attacked during a brake process, the vehicle will lose driving direction control and the driver’s life will be highly threatened. However, current methods for detecting attacks are not sufficiently accurate, and no method can provide attack mitigation. To ensure vehicle ABS security, we propose an attack detection method to accurately detect both sensor attack (SA) and CAN bus attack in a vehicle ABS, and an attack mitigation strategy to mitigate their negative effects on the vehicle ABS. In our attack detection method, we build a vehicle state space equation that considers the real-time road friction coefficient to predict vehicle states (i.e., wheel speed and longitudinal brake force) with their previous values. Based on sets of historical measured vehicle states, we develop a search algorithm to find out attack changes (vehicle state changes because of attack) by minimizing errors between the predicted vehicle states and the measured vehicle states. In our attack mitigation strategy, attack changes are subtracted from the measured vehicle states to generate correct vehicle states for a vehicle ABS. We conducted the first real SA experiments to show how a magnet affects sensor readings. Our simulation results demonstrate that our attack detection method can detect SA and CAN bus attack more accurately compared with existing methods, and also that our attack mitigation strategy almost eliminates the attack’s effects on a vehicle ABS.

References

[1]
Robert N. Charette. 2009. This car runs on code. IEEE Spectrum 46, 3 (2009), 1–3.
[2]
Michael Müter, André Groll, and Felix C. Freiling. 2010. A structured approach to anomaly detection for in-vehicle networks. In Proceedings of the 2010 6th International Conference on Information Assurance and Security(IAS’10).
[3]
Christof Paar and André Weimerskirch. 2007. Embedded security in a pervasive world. Information Security Technical Report 12, 3 (2007), 155–161.
[4]
Yasser Shoukry, Paul Martin, Paulo Tabuada, and Mani Srivastava. 2013. Non-invasive spoofing attacks for anti-lock braking systems. In Cryptographic Hardware and Embedded Systems—CHES 2013. Lecture Notes in Computer Science, Vol. 8086. Springer, 55-72.
[5]
Samuel Woo, Hyo Jin Jo, and Dong Hoon Lee. 2015. A practical wireless attack on the connected car and security protocol for in-vehicle CAN. IEEE Transactions on Intelligent Transportation Systems 16, 2 (2015), 993–1006.
[6]
Charlie Miller and Chris Valasek. 2015. Remote Exploitation of an Unaltered Passenger Vehicle. Technical White Paper. IOActive.
[7]
Buke Ao, Yongcai Wang, Lu Yu, Richard R. Brooks, and S. S. Iyengar. 2016. On precision bound of distributed fault-tolerant sensor fusion algorithms. ACM Computing Surveys 49, 1 (2016), Article 5, 23 pages.
[8]
Junkil Park, Radoslav Ivanov, James Weimer, Miroslav Pajic, and Insup Lee. 2015. Sensor attack detection in the presence of transient faults. In Proceedings of the ACM/IEEE 6th International Conference on Cyber-Physical Systems (ICCPS’15). 1–10.
[9]
Radoslav Ivanov, Miroslav Pajic, and Insup Lee. 2014. Resilient multidimensional sensor fusion using measurement history. In Proceedings of the 3rd ACM International Conference on High Confidence Networked Systems (HiCoNS’14).
[10]
Jongho Shin, Youngmi Baek, Jaeseong Lee, and Seonghun Lee. 2019. Cyber-physical attack detection and recovery based on RNN in automotive brake systems. Applied Sciences 9, 1 (2019), 82.
[11]
Joe Halabi and Hassan Artail. 2018. A lightweight synchronous cryptographic hash chain solution to securing the vehicle CAN bus. In Proceedings of the 2018 IEEE International Multidisciplinary Conference on Engineering Technology (IMCET’18). 1–6.
[12]
Assaf Harel and Amir Hezberg. 2019. Optimizing CAN Bus Security with In-Place Cryptography. Technical Report. SAE.
[13]
Mabrouka Gmiden, Mohamed Hedi Gmiden, and Hafedh Trabelsi. 2019. Cryptographic and intrusion detection system for automotive CAN bus: Survey and contributions. In Proceedings of the 2019 16th International Multi-Conference on Systems, Signals, and Devices (SSD’19). 158–163.
[14]
Ki-Dong Kang, Youngmi Baek, Seonghun Lee, and Sang Hyuk Son. 2017. An attack-resilient source authentication protocol in controller area network. In Proceedings of the Symposium on Architectures for Networking and Communications Systems (ANCS’17). 109–118.
[15]
Kyusuk Han, Swapna Divya Potluri, and Kang G. Shin. 2013. On authentication in a connected vehicle: Secure integration of mobile devices with vehicular networks. In Proceedings of the ACM/IEEE 4th International Conference on Cyber-Physical Systems. 160–169.
[16]
Oliver Cros and Gabriel Chenevert. 2019. Hashing-based authentication for CAN bus and application to denial-of-service protection. In Proceedings of the 2019 3rd Cyber Security in Networking Conference (CSNet’19). IEEE, Los Alamitos, CA, 91–98.
[17]
Michael R. Moore, Robert A. Bridges, Frank L. Combs, Michael S. Starr, and Stacy J. Prowell. 2017. Modeling inter-signal arrival times for accurate detection of CAN bus signal injection attacks: A data-driven approach to in-vehicle intrusion detection. In Proceedings of the 12th Annual Conference on Cyber and Information Security Research. Article 11, 4 pages.
[18]
Tobias Hoppe, Stefan Kiltz, and Jana Dittmann. 2011. Security threats to automotive CAN networks—Practical examples and selected short-term countermeasures. Reliability Engineering & System Safety 96, 1 (2011), 11–25.
[19]
Steve Corrigan. 2008. Introduction to the Controller Area Network (CAN). Application Report, Texas Instruments.
[20]
Karl Koscher, Alexei Czeskis, Franziska Roesner, Shwetak Patel, Tadayoshi Kohno, Stephen Checkoway, Damon McCoy, et al. 2010. Experimental security analysis of a modern automobile. In Proceedings of the 2010 IEEE Symposium on Security and Privacy.
[21]
Kathryn A. Ingle. 1994. Reverse Engineering. McGraw-Hill.
[22]
Charlie Miller and Chris Valasek. 2013. Adventures in Automotive Networks and Control Units. Technical White Paper. IOActive.
[23]
Mario Hrgetic, Josko Deur, Vladimir Ivanovic, and Eric Tseng. 2014. Vehicle sideslip angle EKF estimator based on nonlinear vehicle dynamics model and stochastic tire forces modeling. SAE Journal of Passenger Cars—Mechanical Systems 7, 1 (2014), 86–95.
[24]
Thomas A. Wenzel, K. J. Burnham, M. V. Blundell, and R. A. Williams. 2006. Dual extended Kalman filter for vehicle state and parameter estimation. Vehicle System Dynamics 44, 2 (2006), 153–171.
[25]
A. Alexander and A. Vacca. 2017. Longitudinal vehicle dynamics model for construction machines with experimental validation. International Journal of Automotive and Mechanical Engineering 14, 4 (2017), 4616–4633.
[26]
H. B. Pacejka. 2005. Tire brush model. In Tire and Vehicle Dynamics (3rd ed.). Butterworth-Heinemann, Waltham, MA, 90–127.
[27]
Kyong-Tak Cho, Kang G. Shin, and Taejoon Park. 2015. CPS approach to checking norm operation of a brake-by-wire system. In Proceedings of the ACM/IEEE 6th International Conference on Cyber-Physical Systems. 41–50.
[28]
Kyoungseok Han, Seibum B. Choi, Jonghyup Lee, Dongyoon Hyun, and Jounghee Lee. 2017. Accurate brake torque estimation with adaptive uncertainty compensation using a brake force distribution characteristic. IEEE Transactions on Vehicular Technology 66, 12 (2017), 10830–10840.
[29]
Kanwar Bharat Singh and Saied Taheri. 2015. Estimation of tire–road friction coefficient and its application in chassis control systems. Systems Science & Control Engineering 3, 1 (2015), 39–61.
[30]
Yasser Shoukry and Paulo Tabuada. 2015. Event-triggered state observers for sparse sensor noise/attacks. IEEE Transactions on Automatic Control 61, 8 (2015), 2079–2091.
[31]
Danilo P. Mandic. 2004. A generalized normalized gradient descent algorithm. IEEE Signal Processing Letters 11, 2 (2004), 115–118.
[32]
Keisuke Fujii. 2013. Extended Kalman Filter. ACFA-Sim-J Group.
[33]
CarSim. 2018. CarSim Mechanical Simulation. Retrieved November 24, 2021 from https://www.carsim.com/products/carsim/.
[34]
Google. 2018. Malicious ECU Attacks and Security. Retrieved November 24, 2021 from https://sites.google.com/a/g.ucla.edu/malicious-ecu-attacks-and-security/.
[35]
Long Chen, Mingyuan Bian, Yugong Luo, and Keqiang Li. 2016. Real-time identification of the tyre–road friction coefficient using an unscented Kalman filter and mean-square-error-weighted fusion. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 230, 6 (2016), 788–802.
[36]
Youngmi Baek and Minsu Jo. 2018. Adaptive threshold generation for fault detection with high dependability for cyber-physical systems. Applied Sciences 8, 11 (2018), 2235.
[37]
Kanghyun Nam, Yoichi Hori, and Choonyoung Lee. 2015. Wheel slip control for improving traction-ability and energy efficiency of a personal electric vehicle. Energies 8, 7 (2015), 6820–6840.
[38]
Miroslav Pajic, James Weimer, Nicola Bezzo, Paulo Tabuada, Oleg Sokolsky, Insup Lee, and George J. Pappas. 2014. Robustness of attack-resilient state estimators. In Proceedings of the 2014 ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS’14). IEEE, Los Alamitos, CA, 163–174.
[39]
Kyong-Tak Cho and Kang G. Shin. 2016. Fingerprinting electronic control units for vehicle intrusion detection. In Proceedings of the USENIX Security Symposium.

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  • (2023)CANAttack: Assessing Vulnerabilities within Controller Area NetworkSensors10.3390/s2319822323:19(8223)Online publication date: 2-Oct-2023
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  1. Detection and Mitigation of Sensor and CAN Bus Attacks in Vehicle Anti-Lock Braking Systems

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    Published In

    cover image ACM Transactions on Cyber-Physical Systems
    ACM Transactions on Cyber-Physical Systems  Volume 6, Issue 1
    January 2022
    246 pages
    ISSN:2378-962X
    EISSN:2378-9638
    DOI:10.1145/3492453
    • Editor:
    • Chenyang Lu
    Issue’s Table of Contents

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    Association for Computing Machinery

    New York, NY, United States

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    Publication History

    Published: 06 January 2022
    Accepted: 01 June 2021
    Revised: 01 November 2020
    Received: 01 May 2020
    Published in TCPS Volume 6, Issue 1

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    Author Tags

    1. Vehicle ABS
    2. sensor attack
    3. CAN bus attack
    4. attack detection
    5. attack mitigation

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    Funding Sources

    • U.S. NSF
    • FHWA
    • Microsoft Research Faculty Fellowship
    • Commonwealth Cyber Initiative (CCI)

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    • (2024)Efficient Crypto Engine for Authenticated Encryption, Data Traceability, and Replay Attack Detection Over CAN Bus NetworkIEEE Transactions on Network Science and Engineering10.1109/TNSE.2023.331254511:1(1008-1025)Online publication date: Jan-2024
    • (2023)CANAttack: Assessing Vulnerabilities within Controller Area NetworkSensors10.3390/s2319822323:19(8223)Online publication date: 2-Oct-2023
    • (2023)Multiplicative Attacks with Essential Stealthiness in Sensor and Actuator Loops against Cyber-Physical SystemsSensors10.3390/s2304195723:4(1957)Online publication date: 9-Feb-2023
    • (2023)ICVTest: A Practical Black-Box Penetration Testing Framework for Evaluating Cybersecurity of Intelligent Connected VehiclesApplied Sciences10.3390/app1401020414:1(204)Online publication date: 25-Dec-2023
    • (2023)Introduction to the Special Issue on Automotive CPS Safety & Security: Part 1ACM Transactions on Cyber-Physical Systems10.1145/35799867:1(1-6)Online publication date: 22-Mar-2023
    • (2023)Vehicle Lateral Motion Dynamics Under Braking/ABS Cyber-Physical AttacksIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.329342418(4100-4115)Online publication date: 1-Jan-2023
    • (2023)Unveiling Threats: A Comprehensive Taxonomy of Attacks in In-Vehicle Networks2023 16th International Conference on Security of Information and Networks (SIN)10.1109/SIN60469.2023.10474941(1-7)Online publication date: 20-Nov-2023
    • (2023)Generation of Time-Varying Feedback-Based Wheel Lock Attack Policies with Minimal Knowledge of the Traction DynamicsIntelligent Computing10.1007/978-3-031-37963-5_87(1268-1281)Online publication date: 20-Aug-2023
    • (2022)On the Detectability of the Mode Division-based Anomaly Detector in Industrial Cyber-physical Systems2022 41st Chinese Control Conference (CCC)10.23919/CCC55666.2022.9902218(4337-4342)Online publication date: 25-Jul-2022
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