A Survey of Keystroke Dynamics Biometrics <s> Decision. <s> We developed the keystroke biometric system (KBS) using the statistical features of the discrete wavelet transformed keystroke pattern in the frequency domain in addition to those of the original keystroke pattern in the time domain. Only 20 keystroke patterns of user's password typing, where the length of password is no more than 10, are used for building a KBS. The features in the time domain and those in the frequency domain are separately scored by the rules that we developed, and arbitrary given keystroke patterns are classified on the basis of total scores. The results show that our KBS is competitive in comparison with others due to its cheap computational cost, cheap usability cost, and the practically acceptable classification accuracy. <s> BIB001 </s> A Survey of Keystroke Dynamics Biometrics <s> Decision. <s> This paper presents a novel biologic verification method based on pressure sensor keyboards and classifier fusion techniques. The pressure sensor keyboard is a new product that occurs in the market recently. It produces a pressure sequence when keystroke occurs. The analysis of the pressure sequence should be a novel research area. In this paper, we use the pressure sequence and traditional keystroke dynamics in user authentication. Three methods (global features of pressure sequences, dynamic time warping, and traditional keystroke dynamics) are proposed for the authentication task. We combined the three methods together using a classifier fusion technique at last. Several experiments were performed on a database containing 5000 samples of 100 individuals and the best result were achieved utilizing all the method, obtaining an equal error rate of 1.41%. To make a comparison, the equal error rate is 2.04% when we use only the traditional keystroke dynamics. This approach can be used to improve the usual login-password authentication when the password is no more a secret <s> BIB002 </s> A Survey of Keystroke Dynamics Biometrics <s> Decision. <s> In this paper, we present a novel keystroke dynamic recognition system by means of a novel two-layer fusion approach. First, we extract four types of keystroke latency as the feature from our dataset. The keystroke latency will be transformed into similarity scores via Gaussian Probability Density Function (GPD). We also propose a new technique, known as Direction Similarity Measure (DSM), which measures the absolute difference between two sets of latency. Last, four fusion approaches coupled with six fusion rules are applied to improve the final result by combining the scores that are produced by GPD and DSM. Best result with equal error rate of 1.401% is obtained with our two-layer fusion approach. <s> BIB003
Claimant's feature data is presented to the system and compared to the reference template via classification algorithms. A final decision will be made based upon the outcome of classification or matching algorithm to determine if a user is legitimate or otherwise. Prior to decision making, fusion strategy BIB003 BIB001 BIB002 may be applied to strengthen authentication accuracy.