Network Robustness for Critical Infrastructure Networks

Author(s):  
Anthony H. Dekker ◽  
Bernard Colbert

Events of the past few years have shown how today’s modern technological society is critically dependent on critical infrastructure networks such as telecommunications, transport and power. In this chapter, we examine the robustness of critical infrastructure networks and describe some simulation studies exploring this issue. These studies use an extension of data farming we call “network farming,” implemented within the CAVALIER network analysis tool suite. We then survey some historical data on actual terrorist attacks and show that the distribution of these attacks in time can be modeled by a Poisson statistical distribution. This fact can then be used to plan robust network architectures. We alsoexamine “scale-free networks,” and show how they relate to the robustness of physical and organizational networks. In particular, we study the implications for law-enforcement personnel responding to terrorist organizations, using two historical case studies. Finally, we briefly survey emerging trends in network modeling and intelligent software agents that may influence the robustness of future networks.

2008 ◽  
pp. 1125-1144
Author(s):  
Anthony H. Dekker ◽  
Bernard Colbert

Events of the past few years have shown how today’s modern technological society is critically dependent on critical infrastructure networks such as telecommunications, transport and power. In this chapter, we examine the robustness of critical infrastructure networks and describe some simulation studies exploring this issue. These studies use an extension of data farming we call “network farming,” implemented within the CAVALIER network analysis tool suite. We then survey some historical data on actual terrorist attacks and show that the distribution of these attacks in time can be modeled by a Poisson statistical distribution. This fact can then be used to plan robust network architectures. We alsoexamine “scale-free networks,” and show how they relate to the robustness of physical and organizational networks. In particular, we study the implications for law-enforcement personnel responding to terrorist organizations, using two historical case studies. Finally, we briefly survey emerging trends in network modeling and intelligent software agents that may influence the robustness of future networks.


2011 ◽  
pp. 3609-3627
Author(s):  
Anthony H. Dekker ◽  
Bernard Colbert

Events of the past few years have shown how today’s modern technological society is critically dependent on critical infrastructure networks such as telecommunications, transport and power. In this chapter, we examine the robustness of critical infrastructure networks and describe some simulation studies exploring this issue. These studies use an extension of data farming we call “network farming,” implemented within the CAVALIER network analysis tool suite. We then survey some historical data on actual terrorist attacks and show that the distribution of these attacks in time can be modeled by a Poisson statistical distribution. This fact can then be used to plan robust network architectures. We alsoexamine “scale-free networks,” and show how they relate to the robustness of physical and organizational networks. In particular, we study the implications for law-enforcement personnel responding to terrorist organizations, using two historical case studies. Finally, we briefly survey emerging trends in network modeling and intelligent software agents that may influence the robustness of future networks.


2018 ◽  
Vol 28 (4) ◽  
pp. 735-774 ◽  
Author(s):  
Christopher Burr ◽  
Nello Cristianini ◽  
James Ladyman

2019 ◽  
Author(s):  
James Williams

This paper introduces a novel set of component importance measures that are based on the concept of critical flow. Various research communities have developed techniques for identifying critical components of networks. The methods in this paper extend previous work on flow-based centrality measures by adapting them to the assessment of critical infrastructure in urban systems. The motivation is to provide municipalities with a means of reasoning about the impact of urban interventions. An infrastructure system is represented as a flow network in which demand nodes are assigned both demand values and criticality ratings. Sensitive elements in the network are those that carry critical flows, where a flow is deemed critical to the extent that it satisfies critical demand. A method for computing these flows is presented, and its utility is demonstrated by comparing the new measures to existing flow centrality measures. The paper also shows how the method may be combined with standard approaches to reliability analysis.


Author(s):  
Uros Krcadinac ◽  
Milan Stankovic ◽  
Vitomir Kovanovic ◽  
Jelena Jovanovic

Since the AAAI (http://www.aaai.org) Spring Symposium in 1994, intelligent software agents and agentbased systems became one of the most significant and exciting areas of research and development (R&D) that inspired many scientific and commercial projects. In a nutshell, an agent is a computer program that is capable of performing a flexible, autonomous action in typically dynamic and unpredictable domains (Luck, McBurney, Shehory, & Willmott, 2005). Agents emerged as a response of the IT research community to the new data-processing requirements that traditional computing models and paradigms were increasingly incapable to deal with (e.g., the huge and ever-increasing quantities of available data). Agent-oriented R&D has its roots in different disciplines. Undoubtedly, the main contribution to the field of autonomous agents came from artificial intelligence (AI) which is focused on building intelligent artifacts; and if these artifacts sense and act in some environment, then they can be considered agents (Russell & Norvig, 1995). Also, object-oriented programming (Booch, 2004), concurrent object-based systems (Agha, Wegner, & Yonezawa, 1993), and human-computer interaction (Maes, 1994) are fields that have constantly driven forward the agent R&D in the last few decades.


2009 ◽  
pp. 1452-1457
Author(s):  
Xin Luo ◽  
Somasheker Akkaladevi

Cowan et al. (2002) argued that the human cognitive ability to search for information and to evaluate their usefulness is extremely limited in comparison to those of computers. In detail, it’s cumbersome and time-consuming for a person to search for information from limited resources and to evaluate the information’s usefulness. They further indicated that while people are able to perform several queries in parallel and are good at drawing parallels and analogies between pieces of information, advanced systems that embody ISA architecture are far more effective in terms of calculation power and parallel processing abilities, particularly in the quantities of material they can process (Cowan et al. 2002). According to Bradshaw (1997), information complexity will continue to increase dramatically in the coming decades. He further contended that the dynamic and distributed nature of both data and applications require that software not merely respond to requests for information but intelligently anticipate, adapt, and actively seek ways to support users.


2009 ◽  
pp. 283-302
Author(s):  
Dickson K.W. Chiu ◽  
S.C. Cheun ◽  
Ho-Fung Leung

In a service-oriented enterprise, the professional workforce such as salespersons and support staff tends to be mobile with the recent advances in mobile technologies. There are increasing demands for the support of mobile workforce management (MWM) across multiple platforms in order to integrate the disparate business functions of the mobile professional workforce and management with a unified infrastructure, together with the provision of personalized assistance and automation. Typically, MWM involves tight collaboration, negotiation, and sophisticated business-domain knowledge, and thus can be facilitated with the use of intelligent software agents. As mobile devices become more powerful, intelligent software agents can now be deployed on these devices and hence are also subject to mobility. Therefore, a multiagent information-system (MAIS) infrastructure provides a suitable paradigm to capture the concepts and requirements of an MWM as well as a phased development and deployment. In this book chapter, we illustrate our approach with a case study at a large telecommunication enterprise. We show how to formulate a scalable, flexible, and intelligent MAIS with agent clusters. Each agent cluster comprises several types of agents to achieve the goal of each phase of the workforce-management process, namely, task formulation, matchmaking, brokering, commuting, and service.


Sign in / Sign up

Export Citation Format

Share Document