Disclosing false identity through hybrid link analysis

Combating the identity problem is crucial and urgent as false identity has become a common denominator of many serious crimes, including mafia trafficking and terrorism. Without correct identification, it is very difficult for law enforcement authority to intervene, or even trace terrorists activities. Amongst several identity attributes, personal names are commonly, and effortlessly, falsified or aliased by most criminals. Typical approaches to detecting the use of false identity rely on the similarity measure of textual and other content-based characteristics, which are usually not applicable in the case of highly deceptive, erroneous and unknown descriptions. This barrier can be overcome through analysis of link information displayed by the individual in communication behaviours, financial interactions and social networks. In particular, this paper presents a novel link-based approach that improves existing techniques by integrating multiple link properties in the process of similarity evaluation. It is utilised in a hybrid model that proficiently combines both text-based and link-based measures of examined names to refine the justification of their similarity. This approach is experimentally evaluated against other link-based and text-based techniques, over a terrorist-related dataset, with further generalization to a similar problem occurring in publication databases. The empirical study demonstrates the great potential of this work towards developing an effective identity verification system.