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Intelligent User Profiling Based on Sensors and Location Data to Detect Intrusions on Mobile Devices
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Author(s): Pedro Miguel Sánchez Sánchez (Universidad de Murcia, Spain), José María Jorquera Valero (Universidad de Murcia, Spain), Alberto Huertas Celdran (Waterford Institute of Technology, Ireland)and Gregorio Martínez Pérez (Universidad de Murcia, Spain)
Copyright: 2020
Pages: 25
Source title:
Handbook of Research on Intrusion Detection Systems
Source Author(s)/Editor(s): Brij B. Gupta (National Institute of Technology, Kurukshetra, India)and Srivathsan Srinivasagopalan (AT&T, USA)
DOI: 10.4018/978-1-7998-2242-4.ch001
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Abstract
Continuous authentication systems are considered as a promising solution to secure access to mobile devices. Their main benefit is the improvement of the users' experience when they use the services or applications of their mobile device. Specifically, continuous authentication avoids having to remember or possess any key to access an application or service that requires authentication. In this sense, having the user authenticated permanently increases the security of the device. It also allows the user interaction with applications to be much more fluid, simple, and satisfactory. This chapter proposes a new continuous authentication system for mobile devices. The system acquires data from the device sensors and the GPS location to create a dataset that represents the user's profile or normal behaviour. Then, the proposed system uses Machine Learning algorithms based on anomaly detection to perform user identification in real time. Several experiments have been carried out to demonstrate the performance and usefulness of the proposed solution.
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