Il-Chul Moon, and Kathleen M. Carley, Locating Optimal Destabilization Strategies, International Command and Control Research Technology Symposium (ICCRTS 2007), Newport, Rhode Island, USA, Jun 19-21, 2007

Abstract

Network destabilization is a critical tactic for disrupting organizations such as terrorist networks or organized crime networks. For any network, there are many possible tactics that could be used to destabilize it. For example, a set of nodes (people) could be removed or added. So could the links among the nodes. Even if we limit ourselves to node removal, there are issues which nodes to remove and when. Thus, an automated framework for the generation of the destabilization scenarios is useful, for example, in what-if scenario analysis and vulnerability analysis. We created 1024 randomly generated strategies and 1024 learning algorithm-based strategies for the destabilization of a sample terrorist network. The learning algorithm based strategies were more effective in reducing the performance of the network. Moreover, we analyzed the preference of the learning algorithm and discovered efficient tactics such as undermining the network first and isolating the knowledge sources of a network later. We believe that the presented method can be applied to other disciplines requiring a simulation and a what-if scenario generation involving network destabilization.

@MISC{Moon_goolbylocating,
    author = {Il-chul Moon and Kathleen M. Carley and Il-chul Moon and Kathleen M. Carley},
    title = {Goolby Locating Optimal Destabilization Strategies},
    year = {2007}
}

Source Website: 

 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.150.649&rank=1