Evaluation of Deep Learning Models for Person Authentication Based on Touch Gesture

Touch gesture biometrics authentication system is the study of user's touching behavior on his touch device to identify him.The features traditionally used in touch gesture authentication systems are extracted using hand-crafted feature extraction approach.In this work,we investigate the ability of Deep Learning(DL)to automatically discover useful features of touch gesture and use them to authenticate the user.Four different models are investigated Long-Short Term Memory(LSTM),Gated Recurrent Unit(GRU),Convolutional Neural Network(CNN)combined with LSTM(CNN-LSTM),and CNN combined with GRU(CNN-GRU).In addition,different regularization techniques are investigated such as Activity Regularizer,Batch Normalization(BN),Dropout,and LeakyReLU.These deep networks were trained from scratch and tested using TouchAlytics and BioIdent datasets for dynamic touch authentication.The result reported in terms of authentication accuracy,False Acceptance Rate(FAR),False Rejection Rate(FRR).The best result we have been obtained was 96.73%,96.07%and 96.08%for training,validation and testing accuracy respectively with dynamic touch authentication system on TouchAlytics dataset with CNN-GRU DL model,while the best result of FAR and FRR obtained on TouchAlytics dataset was with CNN-LSTM were FAR was 0.0009 and FRR was 0.0530.For BioIdent dataset the best results have been obtained was 84.87%,78.28%and 78.35%for Training,validation and testing accuracy respectively with CNN-LSTM model.The use of a learning based approach in touch authentication system has shown good results comparing with other state-of-the-art using TouchAlytics dataset.

A data reduction scheme for active authentication of legitimate smartphone owner using informative apps ranking

Smartphones are vulnerable to fraudulent use despite having strong authentication mechanisms.Active authentication based on behavioral biometrics is a solution to protect the privacy of data in smart devices.Machinelearning-based frameworks are effective for active authentication.However,the success of any machine-learningbased techniques depends highly on the relevancy of the data in hand for training.In addition,the training time should be very efficient.Keeping in view both issues,we’ve explored a novel fraudulent user detection method based solely on the app usage patterns of legitimate users.We hypothesized that every user has a unique pattern hidden in his/her usage of apps.Motivated by this observation,we’ve designed a way to obtain training data,which can be used by any machine learning model for effective authentication.To achieve better accuracy with reduced training time,we removed data instances related to any specific user from the training samples which did not contain any apps from the user-specific priority list.An information theoretic app ranking scheme was used to prepare a user-targeted apps priority list.Predictability of each instance related to a candidate app was calculated by using a knockout approach.Finally,a weighted rank was calculated for each app specific to every user.Instances with low ranked apps were removed to derive the reduced training set.Two datasets as well as seven classifiers for experimentation revealed that our reduced training data significantly lowered the prediction error rates in the context of classifying the legitimate user of a smartphone.