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Fuzzy Decision Fusion and Multiformalism Modelling in Physical Security Monitoring

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Recent Advances in Computational Intelligence in Defense and Security

Part of the book series: Studies in Computational Intelligence ((SCI,volume 621))

Abstract

Modern smart-surveillance applications are based on an increasingly large number of heterogeneous sensors that greatly differ in size, cost and reliability. System complexity poses issues in its design, operation and maintenance since a large number of events needs to be managed by a limited number of operators. However, it is rather intuitive that redundancy and diversity of sensors may be advantageously leveraged to improve threat recognition and situation awareness. That can be achieved by adopting appropriate model-based decision-fusion approaches on sensor-generated events. In such a context, the challenges to be addressed are the optimal correlation of sensor events, taking into account all the sources of uncertainty, and how to measure situation recognition trustworthiness. The aim of this chapter is twofold: it deals with uncertainty by enriching existing model-based event recognition approaches with imperfect threat modelling and with the use of different formalisms improving detection performance. To that aim, fuzzy operators are defined using the probabilistic formalisms of Bayesian Networks and Generalized Stochastic Petri Nets. The main original contributions span from support physical security system design choices to the demonstration of a multiformalism approach for event correlation. The applicability of the approach is demonstrated on the case-study of a railway physical protection system.

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Notes

  1. 1.

    The difference between detected and suspected scenario depends on the partial or total matching between the real-time event tree and the stored threat pattern.

  2. 2.

    In this case of event detection, the confusion matrix accounts for binary events which can be true (i.e. occurred) or false. In DETECT, the positive false probability is given by P(a = true | e = false) while the negative false probability is P(a = false | e = true).

References

  1. Garcia, M.L.: The Design and Evaluation of Physical Protection Systems. Butterworth-Heinemann, Boston (2001)

    Google Scholar 

  2. Flammini, F., Gaglione, A., Mazzocca, N., Moscato, V., Pragliola, C.: Wireless sensor data fusion for critical infrastructure security. Adv. Intell. Soft Comput. 53, 92–99 (2009)

    Google Scholar 

  3. Flammini, F., Gaglione, A., Ottello, F., Pappalardo, A., Pragliola, C., Tedesco, A.: Towards wireless sensor networks for railway infrastructure monitoring. In: Proceeding ESARS 2010, pp. 1–6, Bologna, Italy (2010)

    Google Scholar 

  4. Zhu, Z., Huang, T.S.: Multimodal Surveillance: Sensors, Algorithms and Systems. Artech House Publisher, Boston (2007)

    Google Scholar 

  5. Wickens, C., Dixon, S.: The benefits of imperfect diagnostic automation: a synthesis of the literature. Theor. Issues Ergon. Sci. 8(3), 201–212 (2007)

    Article  Google Scholar 

  6. Dong, M., He, D.: Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis Eur. J. Oper. Res. 178(3), 858–878 (2007)

    Google Scholar 

  7. Guo, H., Shi, W., Deng, Y.: Evaluating sensor reliability in classification problems based on evidence theory. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 36(5), 970–981 (2006). doi:10.1109/TSMCB.2006.872269

    Article  Google Scholar 

  8. Luo, H., Tao, H., Ma, H., Das, S.K.: Data fusion with desired reliability in wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 23(3), 501–513 (2012)

    Google Scholar 

  9. 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.) Safety, Reliability and Risk Analysis: Theory, Methods and Applications, Proceedings of ESREL’08, pp. 105–112 (2008)

    Google Scholar 

  10. Flammini, F., Gaglione, A., Mazzocca, N., Moscato, V., Pragliola, C.: On-line integration and reasoning of multi-sensor data to enhance infrastructure surveillance. J. Inf. Assur. Secur. (JIAS) 4(2), 183–191 (2009)

    Google Scholar 

  11. Bobbio, A., Ciancamerla, E., Franceschinis, G., Gaeta, R., Minichino, M., Portinale, L.: Sequential application of heterogeneous models for the safety analysis of a control system: a case study. RESS 81(3), 269–280 (2003)

    Google Scholar 

  12. Flammini, F., Marrone, S., Iacono, M., Mazzocca, N., Vittorini, V.: A Multiformalism Modular Approach to ERTMS/ETCS Failure Modelling. Int. J. Reliab. Qual. Saf. Eng. Vol. 21(1) 450001 World Scientific Publishing Company (2014). doi:10.1142/S0218539314500016

    Google Scholar 

  13. Flammini, F., Marrone, S., Mazzocca, N., Vittorini, V.: A new modelling approach to the safety evaluation of N-modular redundant computer systems in presence of imperfect maintenance. RESS 94(9), 1422–1432 (2009)

    Google Scholar 

  14. Flammini, F., Mazzocca, N., Pappalardo, A., Pragliola, C., Vittorini, V.: Augmenting surveillance system capabilities by exploiting event correlation and distributed attack detection. In: Proceeding 2011 International Workshop on Security and Cognitive Informatics for Homeland Defence (SeCIHD’11), LNCS 6908, pp. 191–204 (2011)

    Google Scholar 

  15. Bocchetti, G., Flammini, F., Pragliola, C., Pappalardo, A.: Dependable integrated surveillance systems for the physical security of metro railways. In: IEEE Proceeding of 3rd ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC 2009), pp. 1–7 (2009)

    Google Scholar 

  16. 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: Proceeding of Workshop on Multimedia Systems for Surveillance (MMSS), pp. 414–419 (2011)

    Google Scholar 

  17. Flammini, F., Pappalardo, A., Vittorini, V.: Challenges and emerging paradigms for augmented surveillance. In: Effective Surveillance for Homeland Security: Combining Technology and Social Issues. Taylor & Francis/CRC Press, Boca Raton (2013) To appear

    Google Scholar 

  18. Räty, T.D.: Survey on contemporary remote surveillance systems for public safety. IEEE Trans. Sys. Man Cyber Part C, No. 40, 5, 493–515 (2010)

    Google Scholar 

  19. Hunt, S.: Physical security information management (PSIM): The basics. http://www.csoonline.com/article/622321/physical-security-information-management-psim-the-basics (2011)

  20. Frost & Sullivan: Analysis of the Worldwide Physical Security Information Management Market. http://www.cnlsoftware.com/media/reports/Analysis_Worldwide_Physical_Security_Information_Management_Market.pdf (2012)

  21. Ortmann, S., Langendoerfer, P: Enhancing reliability of sensor networks by fine tuning their event observation behavior. In: Proceeding 2008 International Symposium on a World of Wireless, Mobile and Multimedia Networks (WOWMOM ‘08). IEEE (2008)

    Google Scholar 

  22. Bahrepour, M., Meratnia, N., Havinga, P.J.M.: Sensor fusion-based event detection in wireless sensor networks. In: 6th Annual International Conference MobiQuitous 2009, 13–16 July 2009

    Google Scholar 

  23. 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)

    Article  Google Scholar 

  24. Legg, J.A.: Distributed multisensor fusion system specification and evaluation issues. Defence Science and Technology Organisation, Edinburgh, South Australia 5111, Australia (2005)

    Google Scholar 

  25. 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: ICDM ‘10, IEEE Computer Society, Washington, USA (2010)

    Google Scholar 

  26. Karimaa, A.: Efficient video surveillance: performance evaluation in distributed video surveillance systems. In: Lin, W., (ed.) Video Surveillance, ISBN: 978-953-307-436-8, InTech (2011)

    Google Scholar 

  27. Flammini, F., Gentile, U., Marrone, S., Nardone, R., Vittorini, V.: A petri net pattern-oriented approach for the design of physical protection systems. In: proceedings of Computer Safety, Reliability, and Security; LNCS 8666, 230–245 (2014)

    Google Scholar 

  28. Drago, A., Marrone, S., Mazzocca, N., Tedesco, A., Vittorini, V.: Model-Driven Estimation of Distributed Vulnerability in Complex Railway Networks. In: Ubiquitous Intelligence and Computing, 2013 IEEE 10th International Conference on Autonomic and Trusted Computing (UIC/ATC), pp.380–387, 18–21 Dec. 2013 doi:10.1109/UIC-ATC.2013.78

  29. Bagheri, E., Ghorbani, A.A.: UML-CI: a reference model for profiling critical infrastructure systems. Inf. Syst. Frontiers 12(2), 115–139 (2010)

    Article  Google Scholar 

  30. Marrone, S., Nardone, R., Tedesco, A., D’Amore, P., Vittorini, V., Setola, R., Cillis, F.D., Mazzocca, N.: Vulnerability modeling and analysis for critical infrastructure protection applications. Int. J. Crit. Infrastruct. Prot. 6(34), 217–227 (2013). doi:http://dx.doi.org/

  31. Rodrìguez, R.J., Merseguer, J., Bernardi, S.: Modelling security of critical infrastructures: a survivability assessment. Comput. J. (2014). doi:10.1093/comjnl/BXU096

    Google Scholar 

  32. Chakravarthy, S., Mishra, D.: Snoop, an expressive event specification language for active databases. Data Knowl. Eng. 14(1), 1–26 (1994)

    Article  Google Scholar 

  33. Charniak, E.: Bayesian Networks without Tears, AI Magazine, 1991

    Google Scholar 

  34. Ajmone-Marsan, M., Balbo, G., Conte, G., Donatelli S., Franceschinis, G.: Modelling with Generalized Stochastic Petri Nets; Wiley Series in Parallel Computing. John Wiley and Sons, New York ISBN: 0–471-93059-8 (1995)

    Google Scholar 

  35. National Consortium for the Study of Terrorism and Responses to Terrorism (START).: Global Terrorism Database [199503200014] (2012). http://www.start.umd.edu/gtd

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Author information

Authors and Affiliations

  1. Ansaldo STS, Innovation Unit, Via Argine 425, Naples, Italy

    Francesco Flammini

  2. Department of Mathematics and Physics, Second University of Naples, Viale Lincoln 5, Caserta, Italy

    Stefano Marrone

  3. Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Via Claudio 21, Naples, Italy

    Nicola Mazzocca & Valeria Vittorini

Authors
  1. Francesco Flammini
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  2. Stefano Marrone
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  3. Nicola Mazzocca
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  4. Valeria Vittorini
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Corresponding author

Correspondence to Stefano Marrone .

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Editors and Affiliations

  1. Larus Technologies Corporation, Ottawa, Ontario, Canada

    Rami Abielmona

  2. Larus Technologies Corporation, Ottawa, Ontario, Canada

    Rafael Falcon

  3. Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada

    Nur Zincir-Heywood

  4. School of Engineering and Information Technology, University of New South Wales, Canberra, Aust Capital Terr, Australia

    Hussein A. Abbass

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Flammini, F., Marrone, S., Mazzocca, N., Vittorini, V. (2016). Fuzzy Decision Fusion and Multiformalism Modelling in Physical Security Monitoring. In: Abielmona, R., Falcon, R., Zincir-Heywood, N., Abbass, H. (eds) Recent Advances in Computational Intelligence in Defense and Security. Studies in Computational Intelligence, vol 621. Springer, Cham. https://doi.org/10.1007/978-3-319-26450-9_4

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  • DOI: https://doi.org/10.1007/978-3-319-26450-9_4

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  • Online ISBN: 978-3-319-26450-9

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