On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
The end-to-end effects of Internet path selection
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Stateful Intrusion Detection for High-Speed Networks
SP '02 Proceedings of the 2002 IEEE Symposium on Security and Privacy
Simulating realistic network worm traffic for worm warning system design and testing
Proceedings of the 2003 ACM workshop on Rapid malcode
MASCOTS '04 Proceedings of the The IEEE Computer Society's 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems
Collaborative Internet Worm Containment
IEEE Security and Privacy
Optimal positioning of active and passive monitoring devices
CoNEXT '05 Proceedings of the 2005 ACM conference on Emerging network experiment and technology
Locating network monitors: Complexity, heuristics, and coverage
Computer Communications
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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Many Critical Infrastructures (CI) use the Internet as a means of providing services to citizens and for dispatching their own transactions. CIs, like many other organizations connected to the Internet, are prone to cyber-attacks. The attacks can originate from their trusted customers or peer CIs. Distributed network intrusion detection systems (NIDS) can be deployed within the network of national Network Service Providers to support cyber-attack mitigation. However, determining the optimal placement of NIDS devices is a complex problem that should take into account budget constraints, network topology, communication patterns, and more. In this paper we model interconnected CIs as a communication overlay network and propose using Group Betweenness Centrality as a guiding heuristic in optimizing placement of NIDS with respect to the overlay network. We analyze the effectiveness of the proposed placement strategy by employing standard epidemiological models and compare it to placement strategies suggested in the literature.