Abstract: Nowadays,smartphones are widely used and stored with sensitive information,and the loss of any personal device can cause fatal information compromise.Thus,the people's attention towards data security has been elevated to a higher level.Considering the delicacy of traditional authentications,this paper investigates an implicit authentication mechanism based on over-sampling and one-class classification,for pattern unlock on smartphones.First,a fusion of time,two-dimensional and three-dimensional sensors is employed,to collect user behavioral biometrics comprehensively.Second,in order to ease the impact caused by noise contained in high-dimensional data,a feature screening,which is composed of feature selection and dimensional compression,is designed.Particularly,in view of the existing limitations of the current binary classification schemes,SVM SMOTE is used to over-sample the user behavioral data,and a one-class classification authentication mechanism is delivered to implement classification,of which the learning process is only based on a single-class diminutive training set.A series of experiments have been conducted on actual data,and results show that the proposed scheme,when only relies on a single-class diminutive training set,performs partially better than the traditional binomial KNN classifier which is trained on large-scale data,in terms of accuracy,FAR,FRR and AUC.

Key words: Diminutive training data set, Gesture pattern, Implicit authentication, One-class support vector machine, Over-sampling