U-stroke pattern modeling for end user identity verification through ubiquitous input device

Abstract

Identity verification on ubiquitous input devices is a major concern to validate end-users, because of mobility of the devices. User device interaction (UDI) is capable to capture end-users’ behavioral nature from their device usage pattern. The primary goal of this paper is to collect heterogeneous parameters of usage patterns from any device and build personal profile with good-recognition capability. This work mainly focuses on finding multiple features captured from the usage of smart devices; so that parameters could be used to compose hybrid profile to verify end-users accurately. In this paper, U-Stroke modeling is proposed to capture behavioral data mainly from smart input devices in ubiquitous environment. In addition to this, concept of CCDA (capture, checking, decision, and action) model is proposed to process U-Stroke data efficiently to verify enduser’s identity. This proposal can draw attention of many researchers working on this domain to extend their research towards this direction.