Skip to main content
SpringerLink
Log in

A Systematic Review on Security Mechanism of Electric Vehicles

  • Conference paper
  • First Online:
Intelligent Systems Design and Applications (ISDA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 717))

  • 240 Accesses

Abstract

A classic protocol for in-vehicle network communication is the Controller Area Network (CAN) bus for electric vehicles. The key characteristics of CAN bus are its simplicity, reliability, and applicability for real-time applications. Unfortunately, the lack of a message authentication mechanism in the CAN bus protocol leaves it opens to numerous cyberattacks, making it easier for attackers to access the network. In this paper existing anomaly detection model based survey is proposed, also proposed model based on one-class SVM is proposed approach is to enhanced security control in EV. Additionally, to demonstrate that the suggested method can be used with existing datasets. The suggested method's independence from the meaning of each message ID and data field, which allows the model to be applied to various CAN datasets, is demonstrated by benchmarking with additional CAN bus traffic datasets.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Kieu, T., Yang, B., Jensen,  C. S.: Outlier detection for multidimensional time series using deep neural networks. In: 19th IEEE International Conference on Mobile Data Management (MDM),  pp. 125–134 (2018)

    Google Scholar 

  2. Sommer, C.,  Hoeer, R., Samwer, M., Gerlich, D.W.: A deep learning and novelty detection framework for rapid phenotyping in high-content screening. Mol. Biol. Cell. 28(23), 3428–3436 (2017)

    Google Scholar 

  3. Sanjay Sharma, C., Krishna, R., Sahay, S.K.: Detection of advanced malware by machine learning techniques. In: Ray, K., Sharma, T.K., Sanyog Rawat, R.K., Saini, A.B. (eds.) Soft Computing: Theories and Applications. AISC, vol. 742, pp. 333–342. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-0589-4_31

    Chapter  Google Scholar 

  4. Avate_pour, O., Malik, H.: State-of-the-art survey on in-vehicle network communication (CAN-Bus) security and vulnerabilities (2018) arXiv:1802.01725

  5. Wang, Q., Sawhney, S.: VeCure: a practical security framework to protect the CAN bus of vehicles. In: 2014 International Conference on the Internet of Things (IOT). Cambridge, MA, USA, pp. 13–18 (2014)

    Google Scholar 

  6. Marchetti, M., Stabili, D., Guido, A., Colajanni, M.: Evaluation of anomaly detection for in-vehicle networks through information-theoretic algorithms. In: 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), pp.1–6 (2016)

    Google Scholar 

  7. Kang, M.-J., Kang, J.-W.: Intrusion detection system using deep neural network for in-vehicle network security, PLoS ONE 11(6), e0155781 (2016)

    Google Scholar 

  8. Theissler, A.: Detecting known and unknown faults in automotive systems using ensemble-based anomaly detection.  Knowl.-Based Syst. 123, 163–173 (2017)

    Google Scholar 

  9. Narayanan, S.N., Mittal, S., Joshi, A.: OBD_securealert: an anomaly detection system for vehicles. In: 2016 IEEE International Conference on Smart Computing (SMARTCOMP), pp. 1–6 (2016)

    Google Scholar 

  10. Cho, K.-T., Shin, K. G.: Fingerprinting electronic control units for vehicle intrusion detection.  In: Proceedings 25th USENIX Security Symposium (USENIX Secur.). Berkeley, CA, USA: USENIX Association (2016)

    Google Scholar 

  11. Tayyab, M ., Hafeez, A,  Malik, H.: Spoo_ng attack on clock based intrusion detection system in controller area networks. In: Proceedings NDIA Ground Vehicle Systems Engineering and Technology Symposium, pp. 1–13 (2018)

    Google Scholar 

  12. Taylor, A., Leblanc, S., Japkowicz, V.: Anomaly detection in automobile control network data with long short-term memory networks. In: Proceedings 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 130–139 (2016)

    Google Scholar 

  13. Li, H., Zhao, L., Juliato, M., Ahmed, S., Sastry, M.R., Yang, L.L.: POSTER: intrusion detection system for in-vehicle networks using sensor correlation and integration. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 2531–2533 (2017) 

    Google Scholar 

  14. Ganesan, A., Rao, J., Shin, K.G.: Exploiting consistency among heterogeneous sensors for vehicle anomaly detection. SAE Tech. Paper 2017-01-1654 (2017)

    Google Scholar 

  15. Pajic, M., et al.: Robustness of attack-resilient state estimators. In: 2014 ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS). IEEE, pp. 163–174 (2014)

    Google Scholar 

  16. Markovitz, M., Wool, M.: Field classification, modeling and anomaly detection in unknown CAN bus networks. Veh. Commun. 9, 43–52 (2017)

    Google Scholar 

  17. Marchetti, M., Stabili, D.: Anomaly detection of CAN bus messages through analysis of ID sequences. In: 2017 IEEE Intelligent Vehicles Symposium. (IV), Los Angeles, CA, USA, pp. 1577–1583 (2017)

    Google Scholar 

  18. Studnia, I., Alata, E., Nicomette, V., Kaâniche, M., Laarouchi, Y.: A language-based intrusion detection approach for automotive embedded networks. Int. J. Embedded Syst. 10(1), 1–11 (2018)

    Google Scholar 

  19. Tomlinson, A., Bryans, J., Shaikh, S.A., Kalutarage, H.K.: Detection of automotive CAN cyber-attacks by identifying packet timing anomalies in time windows. In: 2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), Luxembourg City, Luxembourg, pp. 231–238 (2018)

    Google Scholar 

  20. Martinelli, F., Mercaldo, F., Nardone, V., Santone, A.: Car hacking identification through fuzzy logic algorithms. In:  2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–7 (2017)

    Google Scholar 

  21. Lee, H., Jeong, S. H., Kim, H. K.: OTIDS: a novel intrusion detection system for in-vehicle network by using remote frame. In: Proceedings 2017 15th Annual Conference on Privacy, Security and Trust (PST), pp. 57–60 (2017)

    Google Scholar 

  22. Taylor, A., Japkowicz, N., Leblanc, S.: Frequency-based anomaly detection for the automotive CAN bus. In: Proceedings 2015 World Congress on Industrial Control Systems Security (WCICSS), pp. 45–49  (2015)

    Google Scholar 

  23. Avatefipour, O., et al.: An intelligent secured framework for cyberattack detection in electric vehicles’ CAN bus using machine learning. IEEE Access 7, 127580–127592 (2019)

    Google Scholar 

  24. AYDIN, Ö.: Authentication and Billing Scheme for The Electric Vehicles: EVABS. Uluslararası Yönetim Bilişim Sistemleri ve Bilgisayar Bilimleri Dergisi 6(1), 29–42 (2022) 

    Google Scholar 

  25. Ghanishtha, B., Mohan, H., Singh, R.R.: Towards the future of smart electric vehicles: digital twin technology. Renew. Sustain. Energy Rev. 141, 110801 (2021)

    Google Scholar 

  26. Ishak, M.K., Khan, F.K.: Unique Message Authentication Security Approach based Controller Area Network (CAN) for Anti-lock Braking System (ABS) in Vehicle Network “(EUSPN 2019), Coimbra, Portugal 160 4–7 (2019)

    Google Scholar 

  27. Lokman, S.-F., Othman, A.T., Muhammad-Husaini, A.-B.: Intrusion detection system for automotive Controller Area Network (CAN) bus system: a review. EURASIP J. Wireless Commun. Netw. New York 2019(1), 1–17 (2019)

    Google Scholar 

  28. Wei, P., Wang, B., Dai, X., Li, L., He, F.: A novel intrusion detection model for the CAN bus packet of in-vehicle network based on attention mechanism and autoencoder. Digital Commun. Netw. 9, 14–21 (2022)

    Google Scholar 

  29. Tahsin C., Dönmez, M.: Anomaly detection in vehicular CAN Bus using message Identifier Sequences. IEEE Explore 9, 136243–136252 (2021)

    Google Scholar 

  30. ShiLiang, D., Huo, K., Wu, T.: A CAN bus security testbed framework for automotive cyber-physical systems. Hindawi August 2022 Wireless Communications and Mobile Comput. 1–11 (2022)

    Google Scholar 

  31. Rajapaksha, S., Kalutarage, H., Al-Kadri, M. O., Madzudzo, G., Petrovski, A.V.: Keep the moving vehicle secure: context-aware intrusion detection system for in-vehicle CAN bus security. In: 2022 14th International Conference on Cyber Conflict: Keep Moving! (CyCon), pp. 309–330 (2022)

    Google Scholar 

  32. Hossain, M.D., Inoue, H., Ochiai, H., Fall, D., Kadobayashi, Y.: Long short-term memory-based intrusion detection system for in-vehicle controller area network bus. In: 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), pp. 10–17 (2020)

    Google Scholar 

  33. Luo, J.N., Wu, C.M., Yang, M.H.: A CAN-Bus lightweight authentication scheme. Sensors (Basel). 21(21), 7069 (2021)

    Article  Google Scholar 

  34. Lei, C., et al.: SDAE+Bi-LSTM-Based situation awareness algorithm for the CAN bus of intelligent connected vehicles. Electronics 11(1), 110 (2022)

    Google Scholar 

  35. Moulahi, T., Zidi, S., Alabdulatif, A., Atiquzzaman, M.: Comparative performance evaluation of intrusion detection based on machine learning in in-vehicle controller area network bus. IEEE Access 9, 99595–99605 (2021)

    Article  Google Scholar 

  36. Biradar, N., Mohite, Y.: Security Challenges in Controller Area Network (CAN) in Smart Vehicles: Grenze International Journal of Engineering and Technology, June Issue (2022)

    Google Scholar 

  37. Bozdal, M., Samie, M, Aslam, Jennions, I.: Evaluation of CAN bus security challenges. Sensors 20, 2364 (2020)

    Article  Google Scholar 

  38. Hoppe, T., Kiltz, S., Dittmann, J.: Applying intrusion detection to automotive it-early insights and remaining challenges. J. Inform. Assur. Secur. 4(6), 226–235 (2009)

    Google Scholar 

  39. Larson, U.E., Nilsson, D.K., Jonsson, E.: An Approach to Specification-Based Attack Detection for In-Vehicle Networks. In: Intelligent Vehicles Symposium. In: 2008 IEEE, pp. 220–225 (2008)

    Google Scholar 

  40. Müter, M., Groll, A., Freiling, F.C.: A structured approach to anomaly detection for in-vehicle networks. In: Information Assurance and Security (IAS). (Atlanta, 2010), pp. 92–98 (2010)

    Google Scholar 

  41. Müter, M., Asaj, N.: Entropy-based anomaly detection for in-vehicle networks. In: 2011 IEEE Intelligent Vehicles Symposium (IV). (Baden-Baden, 2011), pp. 1110–1115

    Google Scholar 

  42. Ling, C., Feng, D.: An algorithm for detection of malicious messages on CAN buses. In: 2012 National Conference on Information Technology and Computer Science. (Atlantis Press, Paris) (2012)

    Google Scholar 

  43. Miller, C., Valasek, C.: Adventures in automotive networks and control units. Def. Con. 21, 260–264 (2013)

    Google Scholar 

  44. Miller, C., Valasek, C.: A Survey of Remote Automotive Attack Surfaces. In: Black Hat USA, 2014, p. 94 (2014)

    Google Scholar 

  45. Wasicek, A., Weimerskirch, A.: In: SAE Technical Paper. Recognizing Manipulated Electronic Control Units (No. 2015-01-0202) (2015)

    Google Scholar 

  46. Taylor, A., Leblanc, S., Japkowicz, N.: Anomaly detection in Automobile Control Network Data with Long Short-Term Memory Networks. In: 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA) (2016)

    Google Scholar 

  47. Narayanan, S.N., Mittal, S., Joshi, A.: OBD_SecureAlert: An Anomaly Detection System for Vehicles. In: 2016 IEEE International Conference on Smart Computing (SMARTCOMP), pp. 1–6 (St. Louis, 2016)

    Google Scholar 

  48. Deng, L., Yu, D.: Deep learning: methods and applications. Found. Trends®. Signal Process. 7(3–4), 197–387 (2014)

    Google Scholar 

  49. Cho, K.T., Shin, K.G.: Fingerprinting Electronic Control Units for Vehicle Intrusion Detection. In: 25th {USENIX} Security Symposium ({USENIX} Security 16), pp. 911–927 (Austin, 2016)

    Google Scholar 

  50. Taylor, A., Japkowicz, N., Leblanc, S.: Frequency-Based Anomaly Detection for the Automotive CAN Bus. In: 2015 World Congress on Industrial Control Systems Security (WCICSS), pp. 45–49 (London, 2015)

    Google Scholar 

  51. Gmiden, M., Gmiden, M.H., Trabelsi, H.: An Intrusion Detection Method for Securing In-Vehicle CAN Bus. In: 2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), pp. 176–180 (Sousse, 2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. VIIT, Pune, India

    Vaishali Mishra

  2. BVCOEW, Pune, India

    Sonali Kadam

Authors
  1. Vaishali Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sonali Kadam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vaishali Mishra .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Data Science, FLAME University, Pune, Maharashtra, India

    Ajith Abraham

  2. Center for Smart Computing Continuum, Burgenland, Austria

    Sabri Pllana

  3. University of Bari, Bari, Italy

    Gabriella Casalino

  4. University of Jinan, Jinan, Shandong, China

    Kun Ma

  5. Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Anu Bajaj

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mishra, V., Kadam, S. (2023). A Systematic Review on Security Mechanism of Electric Vehicles. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 717. Springer, Cham. https://doi.org/10.1007/978-3-031-35510-3_55

Download citation

Publish with us

Policies and ethics

Search

Navigation