Keystroke Dynamics Based Authentication Using Information Sets

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.

Developing of keystroke dynamics analyzing systems using a presentation based on state contexts

“…As a result, the function will build a more complex decision boundary than linear methods. The use of the support vector machine in the problem of user identification by keystroke dynamics is considered in (Bhatia & Hanmandlu, 2017).…”

Methods of long-term and short-term adaptation for biometric image of keystroke dynamics

“…To continue the work of such a user in the system, additional measures will be required, such as the suspension of the employee from work, the intervention of the system administrator, or the use of alternative means of authentication / identification. This issue is beyond the scope of this work (BHATIA, HANMANDLU, 2017).…”

Keystroke Dynamics Based Authentication Using Possibilistic Renyi Entropy Features and Composite Fuzzy Classifier

“…Our previous work [16] deals with generation of the information set features from the same measurements of keystroke dynamics but uses the Mamta-Hanman entropy function in [17].…”

“…In our previous work [16] Step 1: Calculate mean Step 2: Calculate mean Step 3: Compute Step 4: Concatenate I 1 and I 2 to form I. Then train any classifier using concatenated I.…”