Keystroke pressure based typing biometrics authentication system by combining ANN and ANFIS-based classifiers

Security of an information system depends to a large extent on its ability to authenticate legitimate users as well as to withstand attacks of various kinds. Confidence in its ability to provide adequate authentication is, however, waning. This is largely due to the wrongful use of passwords by many users. In this paper, the design and development of keystroke pressure-based typing biometrics for individual user's verification which based on the analysis of habitual typing of individuals is discussed. The paper examines the use of maximum pressure exerted on the keyboard and time latency between keystrokes as features to create typing patterns for individual users. Combining both an Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are adopted as classifiers to verify the authorized and unauthorized users based on extracted features of typing biometric. The effectiveness of the proposed system is evaluated based upon False Reject Rate (FRR) and False Accept Rate (FAR). A series of experiment shows that the proposed system that used combined classifiers produces promising result for both FAR and FRR.