Advertisement

Improving the Dependability of Distributed Surveillance Systems Using Diverse Redundant Detectors

  • Francesco FlamminiEmail author
  • Nicola Mazzocca
  • Alfio Pappalardo
  • Concetta Pragliola
  • Valeria Vittorini
Conference paper
  • 396 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 307)

Abstract

Sensor networks nowadays employed in critical monitoring and surveillance applications represent a relevant case of complex information infrastructures whose dependability needs to be carefully assessed. Detection models based on Event Trees provide a simple and effective mean to correlate events in Physical Security Information Management (PSIM) systems. However, as a deterministic modeling approach, Event Trees are not able to address uncertainties in practical applications, like: 1) imperfect threat modelling; 2) sensor false alarms. Regarding point (1), it is quite obvious that real-world threat scenarios can be very variable and it is nearly impossible to consider all the possible combinations of events characterizing a threat. Point (2) addresses the possibility of missed detections due to sensor faults and the positive/nuisance false alarms that any real sensor can generate. In this chapter we describe two techniques that can be adopted to deal with those uncertainties. The first technique is based on Event Tree heuristic distance metrics. It allows to generate warnings whenever a threat scenario is detected and it is similar to the ones in the knowledge base repository. The second technique allows to measure in real-time the estimated trustworthiness of event detection based on: a) sensors false alarm rates; b) uncertainties indices associated to correlation operators. We apply those techniques to case-studies of physical security for metro railways.

Keywords

Physical Security Information Management Dependability Situation Recognition False Alarms Soft Computing Fuzzy Logic 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Garcia, M.L.: The Design and Evaluation of Physical Protection Systems. Butterworth-Heinemann (2001)Google Scholar
  2. 2.
    Bocchetti, G., Flammini, F., Pragliola, C., Pappalardo, A.: Dependable integrated surveillance systems for the physical security of metro railways. In: IEEE Procs. of the Third ACM/IEEE International Conference on Distributed Smart Cameras, pp. 1–7 (2009)Google Scholar
  3. 3.
    Zhu, Z., Huang, T.S.: Multimodal Surveillance: Sensors, Algorithms and Systems. Artech House Publisher (2007)Google Scholar
  4. 4.
    Wickens, C., Dixon, S.: The benefits of imperfect diagnostic automation: a synthesis of the literature. Theoretical Issues in Ergonomics Science 8(3), 201–212 (2007)CrossRefGoogle Scholar
  5. 5.
    Flammini, F., Gaglione, A., Mazzocca, N., Moscato, V., Pragliola, C.: Wireless Sensor Data Fusion for Critical Infrastructure Security. In: Corchado, E., Zunino, R., Gastaldo, P., Herrero, Á. (eds.) CISIS 2008. ASC, vol. 53, pp. 92–99. Springer, Heidelberg (2009)Google Scholar
  6. 6.
    Flammini, F., Gaglione, A., Ottello, F., Pappalardo, A., Pragliola, C., Tedesco, A.: Towards Wireless Sensor Networks for Railway Infrastructure Monitoring. In: Proc. ESARS 2010, Bologna, Italy, pp. 1–6 (2010)Google Scholar
  7. 7.
    Flammini, F., Gaglione, A., Mazzocca, N., Pragliola, C.: DETECT: a novel framework for the detection of attacks to critical infrastructures. In: Martorell, et al. (eds.) Procs. of ESREL 2008, pp. 105–112 (2008)Google Scholar
  8. 8.
    Ortmann, S., Langendoerfer, P.: Enhancing reliability of sensor networks by fine tuning their event observation behaviour. In: Proc. 2008 International Symposium on a World of Wireless, Mobile and Multimedia Networks (WOWMOM 2008), pp. 1–6. IEEE Computer Society, Washington, DC (2008)CrossRefGoogle Scholar
  9. 9.
    Bahrepour, M., Meratnia, N., Havinga, P.J.M.: Sensor Fusion-based Event Detection in Wireless Sensor Networks. In: 6th Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, MobiQuitous 2009, Toronto, Canada (2009)Google Scholar
  10. 10.
    Tang, L.-A., Yu, X., Kim, S., Han, J., Hung, C.-C., Peng, W.-C.: Tru-Alarm: Trustworthiness Analysis of Sensor Networks in Cyber-Physical Systems. In: Proceedings of the 2010 IEEE International Conference on Data Mining (ICDM). IEEE Computer Society, Washington (2010)Google Scholar
  11. 11.
    Legg, J.A.: Distributed Multisensor Fusion System Specification and Evaluation Issues. Defence Science and Technology Organisation, Edinburgh, South Australia 5111, Australia (October 2005)Google Scholar
  12. 12.
    Karimaa, A.: Efficient Video Surveillance: Performance Evaluation in Distributed Video Surveillance Systems. In: Surveillance, V., Lin, W. (eds.). InTech (2011), http://www.intechopen.com/books/video-surveillance/efficient-video-surveillance-performance-evaluation-in-distributed-video-surveillance-systems
  13. 13.
    Silva, I., Guedes, L.A., Portugal, P., Vasques, F.: Reliability and Availability Evaluation of Wireless Sensor Networks for Industrial Applications. Sensors 12(1), 806–838 (2012)CrossRefGoogle Scholar
  14. 14.
    Flammini, F., Mazzocca, N., Pappalardo, A., Pragliola, C., Vittorini, V.: Augmenting surveillance system capabilities by exploiting event correlation and distributed attack detection. In: Tjoa, A.M., Quirchmayr, G., You, I., Xu, L. (eds.) ARES 2011. LNCS, vol. 6908, pp. 191–204. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  15. 15.
    Flammini, F., Pappalardo, A., Pragliola, C., Vittorini, V.: A robust approach for on-line and off-line threat detection based on event tree similarity analysis. In: Proc. Workshop on Multimedia Systems for Surveillance (MMSS) in Conjunction with 8th IEEE International Conference on Advanced Video and Signal-Based Surveillance, pp. 414–419 (2011)Google Scholar
  16. 16.
    Flammini, F., Pappalardo, A., Vittorini, V.: Challenges and emerging paradigms for augmented surveillance. In: Effective Surveillance for Homeland Security: Balancing Technology and Social Issues, pp. 169–198. Taylor & Francis/CRC Press (2013)Google Scholar
  17. 17.
    Räty, T.D.: Survey on contemporary remote surveillance systems for public safety. IEEE Trans. Sys. Man Cyber Part C 5(40), 493–515 (2010)CrossRefGoogle Scholar
  18. 18.
    Hunt, S.: Physical security information management (PSIM): The basics, http://www.csoonline.com/article/622321/physical-security-information-management-psim-the-basics
  19. 19.
    Frost, Sullivan: Analysis of the Worldwide Physical Security Information Management Market (2012), http://www.cnlsoftware.com/media/reports/Analysis_Worldwide_Physical_Security_Information_Management_Market.pdf
  20. 20.
    Chakravarthy, S., Mishra, D.: Snoop, An expressive event specification language for active databases. Data Knowl. Eng. 14(1), 1–26 (1994)CrossRefGoogle Scholar
  21. 21.
    Ben Mrad, A., Maalej, M.A., Delcroix, V., Piechowiak, S., Abid, M.: Fuzzy Evidence in Bayesian Network. In: Proc. Intl Conf. on Soft Computing and Pattern Recognition, pp. 486–491 (2011)Google Scholar
  22. 22.
    Flammini, F., Marrone, S., Mazzocca, N., Pappalardo, A., Pragliola, C., Vittorini, V.: Trustworthiness Evaluation of Multi-sensor Situation Recognition in Transit Surveillance Scenarios. In: Cuzzocrea, A., Kittl, C., Simos, D.E., Weippl, E., Xu, L. (eds.) CD-ARES Workshops 2013. LNCS, vol. 8128, pp. 442–456. Springer, Heidelberg (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Francesco Flammini
    • 1
    Email author
  • Nicola Mazzocca
    • 2
  • Alfio Pappalardo
    • 1
    • 2
  • Concetta Pragliola
    • 1
  • Valeria Vittorini
    • 2
  1. 1.Innovation & Competitiveness UnitAnsaldo STSNaplesItaly
  2. 2.Department of Computer & Systems EngineeringUniversity of Naples “Federico II”NaplesItaly

Personalised recommendations