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

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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