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

Computer Systems Science & Engineering 2022年第8期42卷 465-481页

作者： Asrar Bajaber Mai Fadel Lamiaa Elrefaei Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairo11629Egypt

Touch gesture biometrics authentication system is the study of user's touching behavior on his touch device to identify *** features traditionally used in touch gesture authentication systems are extracted using hand-crafted feature extraction *** this work,we investigate the ability of Deep Learning(DL)to automatically discover useful features of touch gesture and use them to authenticate the *** 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 *** deep networks were trained from scratch and tested using TouchAlytics and BioIdent datasets for dynamic touch *** 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 *** 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 *** use of a learning based approach in touch authentication system has shown good results comparing with other state-of-the-art using TouchAlytics dataset.

关键词： Touch authentication system touch gestures behavioral biometric deep learning classification CNN RNN LSTM

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

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Digital Communications and Networks 2019年第4期5卷 205-213页

作者： Abdulaziz Alzubaidi Swarup Roy Jugal Kalita Department of Computer Science Umm Alqura UniversityAl-Qunfudah28821Saudi Arabia Department of Computer Applications Sikkim UniversityGangtok737102India Department of Information Technology North Eastern Hill UniversityShillong793022India Department of Computer Science University of ColoradoColorado SpringsCO80918USA

Smartphones are vulnerable to fraudulent use despite having strong authentication *** authentication based on behavioral biometrics is a solution to protect the privacy of data in smart ***-based frameworks are effective for active ***,the success of any machine-learningbased techniques depends highly on the relevancy of the data in hand for *** addition,the training time should be very *** in view both issues,we’ve explored a novel fraudulent user detection method based solely on the app usage patterns of legitimate *** hypothesized that every user has a unique pattern hidden in his/her usage of *** by this observation,we’ve designed a way to obtain training data,which can be used by any machine learning model for effective *** 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 *** information theoretic app ranking scheme was used to prepare a user-targeted apps priority *** of each instance related to a candidate app was calculated by using a knockout ***,a weighted rank was calculated for each app specific to every *** with low ranked apps were removed to derive the reduced training *** 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.

关键词： Fraudulent user Machine learning Classification behavioral biometric Smartphone security

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