Advertisement

Combined Danger Signal and Anomaly-Based Threat Detection in Cyber-Physical Systems

  • Viktoriya Degeler
  • Richard French
  • Kevin JonesEmail author
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 169)

Abstract

Increasing number of physical systems being connected to the internet raises security concerns about the possibility of cyber-attacks that can cause severe physical damage. Signature-based malware protection can detect known hazards, but cannot protect against new attacks with unknown attack signatures. Anomaly detection mechanisms are often used in combination with signature-based anti-viruses, however, they too have a weakness of triggering on any new previously unseen activity, even if the activity is legitimate. In this paper, we present a solution to the problem of protecting an industrial process from cyber attacks, having robotic manufacture facilities with automated guided vehicles (AGVs) as our use case. Our solution combines detection of danger signals with anomaly detection in order to minimize mis-labelling of legitimate new behaviour as dangerous.

Keywords

Intrusion detection Anomaly detection Danger Theory Automated Guided Vehicles Cyber-Physical Systems 

References

  1. 1.
    Cani, A., Gaudesi, M., Sanchez, E., Squillero, G., Tonda, A.: Towards automated malware creation: code generation and code integration. In: Proceedings of the 29th Annual ACM Symposium on Applied Computing, pp. 157–160. ACM (2014)Google Scholar
  2. 2.
    Forrest, S., Perelson, A.S., Allen, L., Cherukuri, R.: Self-nonself discrimination in a computer. In: 2012 IEEE Symposium on Security and Privacy, p. 202. IEEE Computer Society (1994)Google Scholar
  3. 3.
    fr Sicherheit in der Informationstechnik (BSI), B.: Die lage der it-sicherheitin deutschland (2014). https://www.bsi.bund.de/SharedDocs/Downloads/DE/BSI/Publikationen/Lageberichte/Lagebericht2014.pdf?__blob=publicationFile
  4. 4.
    Kim, J., Bentley, P.J., Aickelin, U., Greensmith, J., Tedesco, G., Twycross, J.: Immune system approaches to intrusion detection-a review. Natural Comput. 6(4), 413–466 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Langner, R.: Stuxnet: dissecting a cyberwarfare weapon. Secur. Priv. IEEE 9(3), 49–51 (2011)CrossRefGoogle Scholar
  6. 6.
    Manber, U., et al.: Finding similar files in a large file system. In: Usenix Winter, vol. 94, pp. 1–10 (1994)Google Scholar
  7. 7.
    Matzinger, P.: Tolerance, danger, and the extended family. Annu. Rev. Immunol. 12(1), 991–1045 (1994)CrossRefGoogle Scholar
  8. 8.
    Petit, J., Shladover, S.: Potential cyberattacks on automated vehicles. IEEE Trans. Intell. Transp. Syst. 16(2), 546–556 (2015)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016

Authors and Affiliations

  • Viktoriya Degeler
    • 1
  • Richard French
    • 1
  • Kevin Jones
    • 1
    Email author
  1. 1.Airbus Group InnovationsNewportUK

Personalised recommendations