The Use of Open Source Intelligence in the Construction of Covert Social Networks
Open source intelligence is playing an increasing role in helping agencies responsible for national security to determine the characteristics, motivations and intentions of adversary groups that threaten the stability of civil society. Analytic methods th
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Abstract Open source intelligence is playing an increasing role in helping agencies responsible for national security to determine the characteristics, motivations and intentions of adversary groups that threaten the stability of civil society. Analytic methods that are able to assimilate and process the emergent data from such rich sources in a timely fashion are required in order that predictive insights can be made by intelligence specialists. The methods of social network analysis (SNA) have proved particularly useful in organising and representing covert network organisations however, it is a particularly data-hungry technique. Here, we present recent work on a statistical inference method that seeks to maximise the insight that can be gained into the structure of covert social networks from the limited and fragmentary data gathered from intelligence operations or open sources. A protocol for predicting the existence of hidden “key-players” covert in social networks is given.
1 Introduction In recent years, social network analysis (SNA) has begun to play an increasingly prominent role as means of organising and representing intelligence data relating to terrorist, insurgency and organised-crime groups. Network representations are a highly efficient and concise presentation of large complex data sets and admit an immediate visual comprehension of the extent and internal organisation of many of these groups [1–3]. This is important, because network structures are often used to assist in the development of assessments of the capabilities and resilience of such groups by government agencies responsible for ensuring the safety of
C.J. Rhodes () Networks and Complexity Programme, Institute of Mathematical Sciences and Imperial College Institute for Security Science and Technology, Imperial College London, London, UK e-mail: [email protected] U.K. Wiil (ed.), Counterterrorism and Open Source Intelligence, Lecture Notes in Social Networks 2, DOI 10.1007/978-3-7091-0388-3 9, © Springer-Verlag/Wien 2011
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civil society. Furthermore, the structural nature of these network topologies invites further, more detailed, quantitative analysis. The application of network measures, such as centrality or betweenness, or the application of algorithms to detect clusters or sub-groups within networks, for example, promises the possibility of further insight on the relative importance of different individuals or different parts of the network, and also for comparisons to be made between different networks. In most cases, it is straightforward to construct social network topologies and perform analysis or apply algorithms from available data. However, SNA is a datahungry science. In practice, much of the difficulty lies in the data collection process, long before any quantitative SNA is attempted. Significant time and resources have to be expended in order to gather social network data. It is necessary to collect detailed information about the characteristics of individuals within the network and the pre
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