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Detection of Anomalous Behavior in Modern Smartphones Using Software Sensor-Based Data.

ABSTRACT: This paper describes the steps involved in obtaining a set of relevant data sources and the accompanying method using software-based sensors to detect anomalous behavior in modern smartphones based on machine-learning classifiers. Three classes of models are investigated for classification: logistic regressions, shallow neural nets, and support vector machines. The paper details the design, implementation, and comparative evaluation of all three classes. If necessary, the approach could be extended to other computing devices, if appropriate changes were made to the software infrastructure, based upon mandatory capabilities of the underlying hardware.

SUBMITTER: Vladareanu V

PROVIDER: S-EPMC7284384 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Detection of Anomalous Behavior in Modern Smartphones Using Software Sensor-Based Data.

Vlădăreanu Victor V Voiculescu Valentin-Gabriel VG Grosu Vlad-Alexandru VA Vlădăreanu Luige L Travediu Ana-Maria AM Yan Hao H Wang Hongbo H Ruse Laura L

Sensors (Basel, Switzerland) 20200513 10

This paper describes the steps involved in obtaining a set of relevant data sources and the accompanying method using software-based sensors to detect anomalous behavior in modern smartphones based on machine-learning classifiers. Three classes of models are investigated for classification: logistic regressions, shallow neural nets, and support vector machines. The paper details the design, implementation, and comparative evaluation of all three classes. If necessary, the approach could be exten ...[more]

PMID: 32413952

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