Early Warning System for Cascading Effect Control in Energy Control Systems

  • Cristina Alcaraz
  • Angel Balastegui
  • Javier Lopez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6712)


A way of controlling a cascading effect caused by a failure or a threat in a critical system is using intelligent mechanisms capable of predicting anomalous behaviours and also capable of reacting against them in advance. These mechanisms are known as Early Warning Systems (EWSs) and this will be precisely the main topic of this paper. More specifically, we present in this paper an EWS design based on a Wireless Sensor Network (using the ISA100.11a standard) that constantly supervises the application context. This EWS is also based on forensic techniques to provide dynamic learning capacities. As a result, this new approach will aid to provide a reliable control of incidences by offering a dynamic alarm management system, identification of the most suitable field operator to attend an alarm, reporting of causes and responsible operators, and learning from new anomalous situations.


Early Warning System Wireless Sensor Network Forensic Techniques Energy Control Systems SCADA Systems Cascading Effect 


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  1. 1.
    NIST, Smart Grid Cyber Security Strategy and Requirements, The Smart Grid Interoperability Panel-Cyber Security Working Group, Draft NISTIR 7628, U.S. Department of Commerce (2010)Google Scholar
  2. 2.
    Peerenboom, J., Fishe, R.: Analyzing Cross-Sector Interdependencies. In: IEEE Computer Society, HICSS, pp. 112–119. IEEE Computer Society, Los Alamitos (2007)Google Scholar
  3. 3.
    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
  4. 4.
    Mcclanahan, R.: SCADA and IP, Is Network Convergence Really Here? IEEE Industry Applications (2003)Google Scholar
  5. 5.
    Lopez, J., Roman, R., Alcaraz, C.: Analysis of Security Threats, Requirements, Technologies and Standards in Wireless Sensor Network. In: Foundations of Security Analysis and Design V. LNCS, vol. 5705, pp. 289–338. Springer, Heidelberg (2009)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 PP(99), 1–10 (2010)Google Scholar
  7. 7.
    Roman, R., Alcaraz, C., Lopez, J.: The role of Wireless Sensor Networks in the area of Critical Information Infrastructure Protection. Information Security Technical Report 12(1), 24–31 (2007)CrossRefGoogle Scholar
  8. 8.
    ZigBee Alliance, (accessed on May 2010)
  9. 9.
    WirelessHART,, HART Communication Foundation, (accessed on February 2010)
  10. 10.
    ISA100.11a, ISA-100.11a-2009. Wireless systems for Industrial Automation: Process Control and Related Applications, ISA. The International Society of Automation (2009)Google Scholar
  11. 11.
    Bialek, J.: Critical Interrelations between ICT and Electricity System. In: Electricity Security in the Cyber Age: Managing the Increasing Dependence of the Electricity Infrastructure on ICT (NGInfra), Utrecht, The Netherlands (2009)Google Scholar
  12. 12.
    Electricity Grid in U.S. Penetrated By Spies, The WallStreet Journal News registered on (April 2009),
  13. 13.
    Power Failure Blacks Out Much of Brazil, Paraguay, The WallStreet Journal News registered on (November 2009),
  14. 14.
    Salmon, D., Zeller, M., Guzman, A., Mynam, V., Donolo, M.: Mitigating the Aurora Vulnerability With Existing Technology. Schweitzer Engineering Laboratories, Inc. (2007)Google Scholar
  15. 15.
    Critical Infrastructure Warning Information Network, EPCIP,, (accessed on July 2010)
  16. 16.
    Apel, M., Biskup, J., Flegel, U., Meier, M.: Towards early warning systems – challenges, technologies and architecture. In: Rome, E., Bloomfield, R. (eds.) CRITIS 2009. LNCS, vol. 6027, pp. 151–164. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  17. 17.
    Walter, K., Nash, E.: Coupling Wireless Sensor Networks and the Sensor Observation Service - Bridging the Interoperability Gap. In: 12th Agile International Conference on Geographic Information Science (2009)Google Scholar
  18. 18.
    Bastke, S., Deml, M., Schmidt, S.: Internet Early Warning Systems. Overview and Architecture (December 2009)Google Scholar
  19. 19.
    Technologies and Techniques for Early Warning Systems to Monitor and Evaluate Drinking Water Quality: A State-of-the-Art Revie, Office of Research and Development National Homeland Security Research centre. EPA, United States Environmental Protection Agency (August 2005)Google Scholar
  20. 20.
    Goel, A., Shea, M., Ahuja, S., Feng, W., Mailer, D., Walpole, J.: Forensix: A Robust, High-Performance Reconstruction System (2009)Google Scholar
  21. 21.
    Garcia, P., Diaz, J., Macia, G., Vazquez, E.: Anomaly-based network intrusion detection: Techniques, systems and challenges. Computers & Security Journal 28(1-2), 18–28 (2009)CrossRefGoogle Scholar
  22. 22.
    Alcaraz, C., Agudo, I., Fernandez-Gago, C., Roman, R., Fernandez, G., Lopez, J.: Adaptive Dispatching of Incidences Based on Reputation for SCADA Systems. In: Fischer-Hübner, S., Lambrinoudakis, C., Pernul, G. (eds.) TrustBus 2009. LNCS, vol. 5695, pp. 86–94. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  23. 23.
    NIST, Guide to Integrating Forensic Techniques into Incident Response, National Institute of Standards and Technology (2006)Google Scholar
  24. 24.
    Syr, B.K.: Integrating intrusion alert information to aid forensic explanation: An analytical intrusion detection framework for distributive IDS. In: Information Fusion, Elsevier, Amsterdam (2009)Google Scholar
  25. 25.
    Zanero, S., Savaresi, S.: Unsupervised learning techniques for an intrusion detection system. In: SAC 2004: Proceedings of the 2004 ACM Symposium on Applied Computing, pp. 412–419 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Cristina Alcaraz
    • 1
  • Angel Balastegui
    • 1
  • Javier Lopez
    • 1
  1. 1.Computer Science DepartmentUniversity of MalagaMalagaSpain

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