Intelligent Hybrid Approach to False Identity Detection

Combating identity fraud is prominent and urgent since false identity has become the common denominator of all serious crime. Among many identified identity attributes, personal names are commonly falsified or aliased by most criminals and terrorists. Typical approaches to such name disambiguation rely on the text-based similarity measures, which are efficient to some extent, but severely fail to handle highly deceptive and unknown identities. In light of aforementioned shortcoming, this paper presents an intelligent hybrid approach that proficiently combines both contentbased and link-based measures of examined names to refine the justification of their similarity. In particular, a new linkbased method that exploits multiple link properties is introduced and deployed within the proposed hybrid mechanism. The experimental evaluation of this measure and the hybrid model against other link-based and text-based techniques, over a terrorist-related dataset, significantly indicates their great potentials towards an effective verification system.