Abstract
This paper presents keystroke dynamics based authentication system using the information set concept. Two types of membership functions (MFs) are computed: one based on the timing features of all the samples and another based on the timing features of a single sample. These MFs lead to two types of information components (spatial and temporal) which are concatenated and modified to produce different feature types. Two Component Information Set (TCIS) is proposed for keystroke dynamics based user authentication. The keystroke features are converted into TCIS features which are then classified by SVM, Random Forest and proposed Convex Entropy Based Hanman Classifier. The TCIS features are capable of representing the spatial and temporal uncertainties. The performance of the proposed features is tested on CMU benchmark dataset in terms of error rates (FAR, FRR, EER) and accuracy of the features. In addition, the proposed features are also tested on Android Touch screen based Mobile Keystroke Dataset. The TCIS features improve the performance and give lower error rates and better accuracy than that of the existing features in literature.
Highlights
Security is a concern since the advent of the computers
The keystroke features are converted into Two Component Information Set (TCIS) features which are classified by Support Vector Machines (SVM), Random Forest and proposed Convex Entropy Based Hanman Classifier
Two Gaussian membership functions are employed: one using the mean and variance of all the samples which lead to temporal information values and the other using the mean and variance of a single sample which lead to spatial information values
Summary
The need of robust and ubiquitous security systems is more apparent due to widespread use of Internet and rapidly growing online business transactions, e-banking, shopping, social interactions, emails to name a few. Keystroke dynamics based authentication is concerned with analyzing the human typing rhythm and behavior. Keystroke dynamics based authentication system is dependent on the individual typing pattern. It is mainly based on how a user types rather than what the user types on keyboard. Very few datasets like Biochaves [5], Clarksons University Keystroke Dataset [6] are based on free text
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