Continuous User Authentication on Touchscreen Using Behavioral Biometrics Utilizing Machine Learning Approaches

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Author(s): Amany Sarhan (Department of Computers and Control Engineering, Faculty of Engineering, Tanta University, Egypt)and Ahmed Ramadan (Department of Computer and Control Engineering, Faculty of Engineering, Tanta University, Egypt)
Copyright: 2020
Pages: 39
Source title: Handbook of Research on Multimedia Cyber Security
Source Author(s)/Editor(s): Brij B. Gupta (National Institute of Technology, Kurukshetra, India)and Deepak Gupta (LoginRadius Inc., Canada)
DOI: 10.4018/978-1-7998-2701-6.ch013

Keywords: Cyber and Network Security / Information Science Reference / Media & Communications / Multimedia Technology

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Abstract

Nowadays, touchscreen mobile devices make up a larger share in the market, necessitating effective and robust methods to continuously authenticate touch-based device users. A classification framework is proposed that learns the touch behavior of a user and is able afterwards to authenticate users by monitoring their behavior in performing input touch actions. Two models of features are built; the low-level features (stoke-level) model or the high-level abstracted features (session-level) model. In building these models, two different methods for features selection and data classification were weighted features and PCA. Two classification algorithms were used; ANN and SVM. The experimental results indicate the possibility of continuous authentication for touch-input users with higher promises for session-level features than stroke-level features. Authors found out that using weighted features method and artificial neural networks in building the session-level model yields the most efficient and accurate behavioral biometric continuous user authentication.

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Continuous User Authentication on Touchscreen Using Behavioral Biometrics Utilizing Machine Learning Approaches

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