Typing Signature Classification Model for User Identity Verification

摘要

Typing pattern is a behavioral trait of user that is simple, less costly, and workable at any place having only computing device. In this paper, n-graph typing signature is built during user profiling based on keyboard usage pattern. The main aim of this paper is to increase inclusion of number of typing features (both temporal and global) during decision generation and to simplify the procedure of considering missing typing patterns (various monographs, digraphs, etc), which are not enrolled before. A modular classification model collection-storage-analysis (CSA) is designed to identify user. Typing signature becomes adaptive in nature through learning from environment. Module 1 is used for pattern acquisition and processing, and module 2 is used for storage, whereas module 3 is used for analysis. Final decision is generated on the basis of evaluated match score and enrolled global parameters. Proposed CSA model is capable to reduce space and time overhead in terms of dynamic pattern acquisition and storage without using any approximation method. A customized editor HCI is designed for physical key-based devices to build our own data set. Proposed CSA model can classify typing signature of valid and invalid user without incurring high overhead.