PDR: A Prevention, Detection and Response Mechanism for Anomalies in Energy Control Systems

  • Cristina Alcaraz
  • Meltem Sönmez Turan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7722)


Prevention, detection and response are nowadays considered to be three priority topics for protecting critical infrastructures, such as energy control systems. Despite attempts to address these current issues, there is still a particular lack of investigation in these areas, and in particular in dynamic and automatic proactive solutions. In this paper we propose a mechanism, which is called PDR, with the capability of anticipating anomalies, detecting anomalous behaviours and responding to them in a timely manner. PDR is based on a conglomeration of technologies and on a set of essential components with the purpose of offering situational awareness irrespective of where the system is located. In addition, the mechanism can also compute its functional capacities by evaluating its efficacy and precision in the prediction and detection of disturbances. With this, the entire system is able to know the real reliability of its services and its activity in remote substations at all times.


Detection Energy Control Systems Industrial Wireless Sensor Networks MANET Prevention Response The Internet and Wide-Area Situational Awareness 


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  1. 1.
    Alcaraz, C., Lopez, J.: Analysis of Requirements for Critical Control Systems. In: Sixth IFIP WG 11.10 International Conference on Critical Infrastructure Protection. National Defense University, Washington DC (2012)CrossRefGoogle Scholar
  2. 2.
    Atputharajah, A., Saha, T.K.: Power System Blackouts - Literature Review. In: International Conference on Industrial and Information Systems (ICIIS), pp. 460–465 (2009)Google Scholar
  3. 3.
    NIST. NIST Framework and Roadmap for Smart Grid Interoperability Standards, Release 2.0. NIST Special Publication 1108R2 (February 2012)Google Scholar
  4. 4.
    ANSI/ISA-99.02.01-2009 Standard. Security for Industrial Automation and Control Systems Part 2: Establishing an Industrial Automation and Control Systems Security Program (2009)Google Scholar
  5. 5.
    Alcaraz, C., Lopez, J., Zhou, J., Roman, R.: Secure SCADA Framework for the Protection of Energy Control Systems. Concurrency and Computation Practice & Experience 23(12), 1414–1430 (2011)CrossRefGoogle Scholar
  6. 6.
    Alcaraz, C., Lopez, J.: A Security Analysis for Wireless Sensor Mesh Networks in Highly Critical Systems. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 40(4), 419–428 (2010)CrossRefGoogle Scholar
  7. 7.
    Roman, R., Lopez, J., Gritzalis, S.: Situation Awareness Mechanisms for Wireless Sensor Networks. IEEE Communications Magazine 46(4), 102–107 (2008)CrossRefGoogle Scholar
  8. 8.
    Weisong, H., Hongmei, X.: Large-scale wireless sensor networks situation awareness using multivariate time series association rules mining. In: 2010 International Conference on Communications, Circuits and Systems (ICCCAS), pp. 95–97 (2010)Google Scholar
  9. 9.
    Peerenboom, J., Fisher, R.: Analyzing Cross-Sector Interdependencies. In: HICSS, pp. 112–119. IEEE Computer Society (2007)Google Scholar
  10. 10.
    Güngör, V., Lu, B., Hancke, G.: Opportunities and Challenges of Wireless Sensor Networks in Smart Grid. IEEE Transactions on Industrial Electronics 57(10), 3557–3564 (2010)CrossRefGoogle Scholar
  11. 11.
    Oxford Dictionary. Anomaly, (retrieved on March 2012)
  12. 12.
    Zhou, Y., Fang, Y., Zhang, Y.: Securing Wireless Sensor Networks: a Survey. IEEE Communications Surveys Tutorials 10(3), 6–28 (2008)CrossRefGoogle Scholar
  13. 13.
    ZigBee Alliance. ZigBee PRO, (retrieved on March 2012)
  14. 14.
    HART. WirelessHART Technology, (retrieved on March 2012)
  15. 15.
    Ebrahimi, M.S., Daraei, M.H., Behzadan, V., Khajooeizadeh, A., Behrostaghi, S.A., Tajvidi, M.: A novel utilization of cluster-tree wireless sensor networks for situation awareness in smart grids. In: Innovative Smart Grid Technologies Asia, pp. 1–5 (2011)Google Scholar
  16. 16.
    Gupta, G., Younis, M.: Fault-tolerant Clustering of Wireless Sensor Networks. IEEE Wireless Communications and Networking 3, 1579–1584 (2003)Google Scholar
  17. 17.
    Salfner, F.: Event-based Failure Prediction An Extended Hidden Markov Model Approach. PhD thesis, Humboldt-Universitätzu Berlin (2008)Google Scholar
  18. 18.
    Lopez, J., Alcaraz, C., Najera, P., Roman, R.: Wireless Sensor Networks and the Internet of Things: Do We Need a Complete Integration? In: First International Workshop on the Security of the Internet of Things (SecIoT 2010), Tokyo, Japan (2010)Google Scholar
  19. 19.
    Zhu, W., Xiang, Y., Zhou, J., Deng, R., Bao, F.: Secure Localization with Attack Detection in Wireless Sensor Networks. IJIS 10, 155–171 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Cristina Alcaraz
    • 1
  • Meltem Sönmez Turan
    • 1
  1. 1.National Institute of Standards and TechnologyGaithersburgUSA

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