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Enabling Convergence of Physical and Logical Security Through Intelligent Event Correlation

  • Gianfranco CerulloEmail author
  • Luigi Coppolino
  • Salvatore D’Antonio
  • Valerio Formicola
  • Gaetano Papale
  • Bruno Ragucci
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 616)

Abstract

Until now, in most organizations, physical access systems and logical security systems have operated as two independent elements, and have been managed by completely separate departments. The lack of interoperability between the two sectors often resulted in a security hole of the overall infrastructure. An attacker who has physical access can not only steal a PC or confidential data, but can also compromise network security. Therefore, a combination of physical and logical security definitively allows for a more effective protection of the organization. In this work we present a correlation system which aims at bringing a significant advancement in the convergence of physical and logical security technologies. By “convergence” we mean effective cooperation (i.e. a coordinated and results-oriented effort to work together) among previously disjointed functions. The holistic approach and enhanced awareness technology of our solution allows dependable (i.e. accurate, timely, and trustworthy) detection and diagnosis of attacks. This ultimately results in the achievement of two goals of paramount importance, and precisely guaranteeing the protection of citizens and assets, and improving the perception of security by citizens. The effectiveness of the proposed solution is demonstrated in a scenario that deals with the protection of a real Critical Infrastructure. Three misuse cases have been implemented in a simulation environment in order to show how the correlation system allows for the detection of different attack patterns.

Keywords

Data Fusion Intrusion Detection System Critical Infrastructure Misuse Case Security Operation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The research leading to these results has received funding from the European Commission within the context of the Seventh Framework Programme (FP7/2007–2013) under Grant Agreement No. 313034 (Situation AWare Security Operation Center, SAWSOC Project). It has been also partially supported by the TENACE PRIN Project (n. 20103P34XC) funded by the Italian Ministry of Education, University and Research, and by the Embedded Systems in critical domains POR Project (CUP B25B09000100007) funded by the Campania region in the context of the POR Campania FSE 2007–2013, Asse IV and Asse V.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Gianfranco Cerullo
    • 1
    Email author
  • Luigi Coppolino
    • 1
  • Salvatore D’Antonio
    • 1
  • Valerio Formicola
    • 1
  • Gaetano Papale
    • 1
  • Bruno Ragucci
    • 1
  1. 1.University of Naples “Parthenope”NapoliItaly

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