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Machine Learning as an Enabler of Continuous and Adaptive Authentication in Multimedia Mobile Devices

Machine Learning as an Enabler of Continuous and Adaptive Authentication in Multimedia Mobile Devices
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Author(s): José María Jorquera Valero (Universidad de Murcia, Spain), Pedro Miguel Sánchez Sánchez (Universidad de Murcia, Spain), Alberto Huertas Celdran (Waterford Institute of Technology, Ireland) and Gregorio Martínez Pérez (Universidad de Murcia, Spain)
Copyright: 2020
Pages: 27
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.ch002

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Abstract

Continuous authentication systems allow users not to possess or remember something to authenticate themselves. These systems perform a permanent authentication that improves the security level of traditional mechanisms, which just authenticate from time to time. Despite the benefits of continuous authentication, the selection of dimensions and characteristics modelling of user's behaviour, and the creation and management of precise models based on Machine learning, are two important open challenges. This chapter proposes a continuous and adaptive authentication system that uses Machine Learning techniques based on the detection of anomalies. Applications usage and the location of the mobile device are considered to detect abnormal behaviours of users when interacting with the device. The proposed system provides adaptability to behavioural changes through the insertion and elimination of patterns. Finally, a proof of concept and several experiments justify the decisions made during the design and implementation of this work, as well as demonstrates its suitability and performance.

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