Social and Cyber Factors Interacting over the Infrastructures: A MAS Framework for Security Analysis

  • E. BompardEmail author
  • R. Napoli
  • F. Xue
Part of the Intelligent Systems, Control and Automation: Science and Engineering book series (ISCA, volume 42)


Critical infrastructures are usually characterized by a network structure in which many technical devices interact on a physical layer, being monitored and controlled throughout a cyber network in charge of conveying commands/measurements from/to the decision making centers run by the human decision makers or automatic controllers at the top of this complex system.

This chapter presents a general framework for the analysis of the security of critical infrastructures in terms of three different interacting layers: the physical layer, the cyber layer, and the decision-making layer. In this framework a multi-agent system is introduced to model the interaction of the various players, and the analysis of the security control against natural failures or malicious attacks is conceptually discussed with reference to the interconnected power systems. With each layer is associated a set of metrics able to characterize the layer with respect to its operation and security and that can account for the interactions among the various layers. In this chapter we will show a possible application for the quantitative assessment of the impact of information in system security, by comparing different information scenarios and then identifying and ranking the most critical information.

The aim of the framework is to provide a comprehensive viewpoint of the system robustness or security which takes into account not only physical operation but also the cyber and social (organizational and human) factors to support better security analysis for critical infrastructures.


Power System Multiagent System Physical Layer Critical Infrastructure Reliability Engineer 
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.


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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Dipartimento di Ingegneria ElettricaPolitecnico di TorinoTorinoItaly

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