Pin Shen Teh

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Research on keystroke dynamics biometrics has been increasing, especially in the last decade. The main motivation behind this effort is due to the fact that keystroke dynamics biometrics is economical and can be easily integrated into the existing computer security systems with minimal alteration and user intervention. Numerous studies have been conducted(More)
In this paper we study the performance and effect of diverse keystroke feature combinations on keystroke dynamics authentication system by using fusion approach. First of all, four types of keystroke features are acquired from our collected dataset, later then transformed into similarity scores by using Gaussian Probability Density Function (GPD) and(More)
Keystroke dynamics refers to a user’s habitual typing characteristics. These typing characteristics are believed to be unique among large populations. In this paper, we present a novel keystroke dynamic recognition system by using a fusion method. Firstly, we record the dwell time and the flight time as the feature data. We then calculate their mean and(More)
In this paper, we present a novel keystroke dynamic recognition system by means of a novel two-layer fusion approach. First, we extract four types of keystroke latency as the feature from our dataset. The keystroke latency will be transformed into similarity scores via Gaussian Probability Density Function (GPD). We also propose a new technique, known as(More)
There have been research activities in the area of keystroke dynamics biometrics on physical keyboards (desktop computers or conventional mobile phones) undertaken in the past three decades. However, in terms of touch dynamics biometrics on virtual keyboards (modern touchscreen mobile devices), there has been little published work. Particularly, there is a(More)
This paper reports the performance and effect of diverse keystroke features combination on keystroke dynamic authentication system by using fusion scheme. First of all, four types of keystroke features are acquired from our collected dataset, later then transformed into similarity scores by the use of Gaussian Probability Density Function (GPD) and(More)
Mobile devices have become an integral part of our routine activities. Some of the activities involve the storage or access of sensitive data (e.g. on-line banking, paperless prescription services, etc.). These mobile electronic services (e-Services) typically require a method to securely identify and authenticate a claimed identity. Currently, e-Services(More)
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