Guaranteed Security and Trustworthiness in Transportation Cyber-Physical Systems

Chapter
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Abstract

Transportation cyber-physical systems (CPSs) have the potential to improve traffic safety, mobility, and environmental protection. However, they are subject to threats stemming from increasing reliance on information and communication technologies (ICT). Cybersecurity threats exploit the increased complexity and connectivity of the transportation-critical infrastructure system, placing the transportation at risk. This chapter reviews the state of the art and the state of the practice of CPS in various transportation sectors, including highway, railway, and air. This chapter also examines various cybersecurity threats to the transportation CPS and the current countermeasures to enhance cybersecurity of these CPS. It then discusses several challenges and opportunities in achieving secure and trustworthy transportation CPS.

Notes

Acknowledgements

This research is sponsored by the National Natural Science Foundation of China (No. 61371185) and China Postdoctoral Science Foundation (No. 2015M571231).

References

  1. 1.
    Antsaklis, P. J., et al. (2012). Cyber-physical systems design using dissipativity. Control conference (CCC), 31, 2012. Chinese, IEEE.Google Scholar
  2. 2.
    Ma, H.-D. (2011). Internet of things: Objectives and scientific challenges. Journal of Computer science and Technology, 26(6), 919–924.CrossRefGoogle Scholar
  3. 3.
    Rawat, D. B., Bajracharya, C., & Yan, G. (2015). Towards intelligent transportation cyber-physical systems: Real-time computing and communications perspectives. SoutheastCon 2015. IEEE.Google Scholar
  4. 4.
    Crowcroft, J., & Oechslin, P. (1998). Differentiated end-to-end internet services using a weighted proportional fair sharing TCP. ACM SIGCOMM Computer Communication Review, 28(3), 53–69.CrossRefGoogle Scholar
  5. 5.
    Lu, Y., et al. (2013). On the application development of 3G technology in automobiles. In Proceedings of the FISITA 2012 world automotive congress. Berlin Heidelberg: Springer.Google Scholar
  6. 6.
    Becker, T., et al. (2006). Natural and intuitive multimodal dialogue for in-car applications: The SAMMIE system. Frontiers in Artificial Intelligence and Applications, 141, 612.Google Scholar
  7. 7.
    Zheng, B., et al. (2015). Design and verification for transportation system security. In Proceedings of the 52nd annual design automation conference. ACM.Google Scholar
  8. 8.
    Qureshi, K. N., & Abdullah, A. H. (2013). A survey on intelligent transportation systems. Middle-East Journal of Scientific Research, 15(5), 629–642.Google Scholar
  9. 9.
    Schoitsch, E., & Skavhaug, A. (2014). Introduction: ERCIM/EWICS/ARTEMIS workshop on dependable embedded and cyberphysical systems and systems-of-systems (DECSoS’ 14) at SAFECOMP 2014. International conference on computer safety, reliability, and security. Springer International Publishing.Google Scholar
  10. 10.
    Platzer, A. (2009). Verification of cyberphysical transportation systems. IEEE Intelligent Systems, 24(4), 10–13.CrossRefGoogle Scholar
  11. 11.
    Bu, L., et al. (2012). Demo abstract: Bachol-modeling and verification of cyber-physical systems online. In Proceedings of the 2012 IEEE/ACM third international conference on cyber-physical systems. IEEE Computer Society.Google Scholar
  12. 12.
    Yampolskiy, M., et al. (2012). Systematic analysis of cyber-attacks on CPS-evaluating applicability of DFD-based approach. In 5th international symposium on resilient control systems (ISRCS), 2012. IEEE.Google Scholar
  13. 13.
    Brenwald, J. (2011). Vehicle data acquisition. Group 14, 11–7.Google Scholar
  14. 14.
    Cohen, F. (1999). Simulating cyber attacks, defences, and consequences. Computers and Security, 18(6), 479–518.CrossRefGoogle Scholar
  15. 15.
    Cheung, S., Lindqvist, U., & Fong, M. W. (2003). Modeling multistep cyber attacks for scenario recognition. DARPA information survivability conference and exposition, 2003. Proceedings (Vol. 1). IEEE.Google Scholar
  16. 16.
    Chen, T. M., Sanchez-Aarnoutse, J. C., & Buford, J. (2011). Petri net modeling of cyber-physical attacks on smart grid. IEEE Transactions on Smart Grid 2.4, 741–749.Google Scholar
  17. 17.
    Spitzner, L. (2003). Honeypots: tracking hackers (Vol. 1). Reading: Addison-Wesley.Google Scholar
  18. 18.
    Lu, S., et al. (2009). SAODV: a MANET routing protocol that can withstand black hole attack. International conference on computational intelligence and security, 2009. CIS’ 09 (Vol. 2). IEEE.Google Scholar
  19. 19.
    Hu, Y.-C., Perrig, A., & Johnson, D. B. (2005). Ariadne: A secure on-demand routing protocol for ad hoc networks. Wireless Networks, 11(1-2), 21–38.CrossRefGoogle Scholar
  20. 20.
    Wu, T. D. (1998). The secure remote password protocol. NDSS (Vol. 98).Google Scholar
  21. 21.
    Yao, C. C. (1982). Protocols for secure computations (extended abstract), Pcr—O(1/n) = O(1) points from P Pcr in Si, since Pr[p Si] = (n 1/c) is, 160–164.Google Scholar
  22. 22.
    Zhang, X., Wang, X., Liu, A., Zhang, Q., & Tang, C. (2012). Pri: A practical reputation-based incentive scheme for delay tolerant networks. Ksii Transactions on Internet and Information Systems, 6(4), 973–988.Google Scholar
  23. 23.
    Lu, X., Hui, P., Towsley, D., Pu, J., & Xiong, Z. (2010). Anti-localization anonymous routing for delay tolerant network. Computer Networks the International Journal of Computer and Telecommunications Networking, 54(11), 1899–1910.MATHGoogle Scholar
  24. 24.
    Lin, X., Lu, R., Liang, X., & Shen, X. (2011). Stap: A social-tier-assisted packet forwarding protocol for achieving receiver-location privacy preservation in vanets. Proceedings IEEE INFOCOM, 28(6), 2147–2155.Google Scholar
  25. 25.
    He, Z., Cai, Z., & Wang, X. (2015). Modeling propagation dynamics and developing optimized countermeasures for rumor spreading in online social networks. The 35th IEEE international conference on distributed computing systems (pp. 205–214).Google Scholar
  26. 26.
    Wang, Y., Cai, Z., Yin, G., Gao, Y., & Pan, Q. (2016). A game theory-based trust measurement model for social networks. Computational Social Networks.Google Scholar
  27. 27.
    Wang, Y., Yin, G., Cai, Z., Dong, Y., & Dong, H. (2015). A trust-based probabilistic recommendation model for social networks. Journal of Network and Computer Applications, 55, 59–67.Google Scholar
  28. 28.
    Sun, Y., & Jara, A. J. (2014). An extensible and active semantic model of information organizing for the internet of things. Personal and Ubiquitous Computing, 18(8), 1821–1833.Google Scholar
  29. 29.
    Lee, E. A. (2010). CPS foundations. In Proceedings of the 47th design automation conference. ACM.Google Scholar

Copyright information

© The Author(s) 2017

Authors and Affiliations

  1. 1.Beijing Normal UniversityBeijingChina

Personalised recommendations