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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/27806
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| Title: | Suspicion scoring based on guilt-by-association, collective inference,
and focused |
| Authors: | Mcskassy, Sofus
Provost, Foster |
| Keywords: | predictive analysis
link analysis
social network analysis
counter-terrorism |
| Issue Date: | 2005 |
| Citation: | Proceedings of the 2005 International Conference on Intelligence Analysis |
| Series/Report no.: | CeDER-PP-2005-04 |
| Abstract: | We describe a guilt-by-association system that can be used to rank
entities by their suspiciousness. We demonstrate the algorithm on a
suite of data sets generated by a terroristworld simulator developed
under a DoD program. The data sets consist of thousands of people and
some known links between them. We show that the system ranks truly
malicious individuals highly, even if only relatively few are known to
be malicious ex ante. When used as a tool for identifying promising
data-gathering opportunities, the system focuses on gathering more
information about the most suspicious people and thereby increases the
density of linkage in appropriate parts of the network. We assess
performance under conditions of noisy prior knowledge (score quality
varies by data set under moderate noise), and whether augmenting the
network with prior scores based on profiling information improves the
scoring (it doesn’t). Although the level of performance reported
here would not support direct action on all data sets, it does recommend
the consideration of network-scoring techniques as a new source of
evidence in decision making. For example, the system can operate on
networks far larger and more complex than could be processed by a human analyst. |
| URI: | http://hdl.handle.net/2451/27806 |
| Appears in Collections: | CeDER Published Papers
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