Keystroke Biometric Systems for User Authentication

Abstract

Keystroke biometrics (KB) authentication systems are a less popular form of access control, although they are gaining popularity. In recent years, keystroke biometric authentication has been an active area of research due to its low cost and ease of integration with existing security systems. Various researchers have used different methods and algorithms for data collection, feature representation, classification, and performance evaluation to measure the accuracy of the system, and therefore achieved different accuracy rates. Although recently, the support vector machine is most widely used by researchers, it seems that ensemble methods and artificial neural networks yield higher accuracy. Moreover, the overall accuracy of KB is still lower than other biometric authentication systems, such as iris. The objective of this paper is to present a detailed survey of the most recent researches on keystroke dynamic authentication, the methods and algorithms used, the accuracy rate, and the shortcomings of those researches. Finally, the paper identifies some issues that need to be addressed in designing keystroke dynamic biometric systems, makes suggestions to improve the accuracy rate of KB systems, and proposes some possible future research directions.