Keystroke Dynamics User Authentication Using Advanced Machine Learning Methods
Yunbin Deng, Yu Zhong · Gate to Computer Science and Research · 2015
User authentication based on typing patterns offers many advantages in the domain of cyber security, including data acquisition without extra hardware requirement, continuous monitoring as the keys are typed, and non-intrusive operation with no interruptions to a user's daily work.In this chapter, we adopt three popular voice biometrics algorithms to perform keystroke dynamics based user authentication, namely, 1) Gaussian Mixture Model with Universal Background Model (GMM-UBM), 2) identity vector (i-vector) approach to user modelling, and 3) deep machine learning approach.Unlike most existing keystroke biometrics approaches, which only use genuine users' data at training time, the proposed methods leverage data from a large pool of background users to enhance the model's discriminative capability.These algorithms make no assumption about the underlying probability distribution of the data and are amenable to real-time implementation.Although these techniques were originally developed for speech analysis, our experiments on the publicly available CMU keystroke dynamics dataset using these algorithms have shown significant reduction in the equal error