Sang, Y., Shen, H. and Fan, P. (2004) Novel Impostors Detection in Keystroke Dynamics by Support Vector Machine. In: Parallel and Distributed Computing: Applications and Technologies, Springer, Berlin, 666-669.
https://doi.org/10.1007/978-3-540-30501-9_128

has been cited by the following article:

• TITLE: Keystroke Dynamics Based Authentication Using Information Sets

  AUTHORS: Aparna Bhatia, Madasu Hanmandlu

  KEYWORDS: Information Set Theory, Two Component Information Set Features, Support Vector Machines (SVM), Random Forest, Convex Hanman-Anirban Entropy Function, Hanman Classifier, Convex Entropy Based Classifier

  JOURNAL NAME: Journal of Modern Physics, Vol.8 No.9, August 23, 2017

  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.