In the wake of recent terrorist atrocities, intelligence experts have commented that failures in detecting terrorist and criminal activities are not so much due to a lack of data, as they are due to difficulties in relating and interpreting the available intelligence. An intelligent tool for monitoring and interpreting intelligence data will provide a helpful means for intelligence analysts to consider emerging scenarios of plausible threats, thereby offering useful assistance in devising and deploying preventive measures against such possibilities. One of the major problems in need of such attention is detecting false identity that has become the common denominator of all serious crime, especially terrorism. Typical approaches to this problem rely on the similarity measure of textual and other content-based characteristics, which are usually not applicable in the case of deceptive and erroneous description. This barrier may be overcome through link information presented in communication behaviors, financial interactions and social networks. Quantitative link-based similarity measures have proven effective for identifying similar problems in the Internet and publication domains. However, these numerical methods only concentrate on link structures, and fail to achieve accurate and coherent interpretation of the information. Inspired by this observation, the chapter presents a novel qualitative similarity measure that makes use of multiple link properties to refine the underlying similarity estimation process and consequently derive semanticrich similarity descriptors. The approach is based on order-of-magnitude reasoning. Its performance is empirically evaluated over a terrorism-related dataset, and compared against several state-of-the-art link-based algorithms and other alternative methods.
|Title of host publication
|Social Network Mining, Analysis, and Research Trends
|Subtitle of host publication
|Techniques and Applications
|Tzung-Pei Hong, Leon S. L. Wang, I-Hsien Ting
|Number of pages
|Published - 2012