Improving the Security and QoE in Mobile Devices through an Intelligent and Adaptive Continuous Authentication System

Continuous authentication systems for mobile devices focus on identifying users according to their behaviour patterns when they interact with mobile devices. Among the benefits provided by these systems, we highlight the enhancement of the system security, having permanently authenticated the users;...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2018-11, Vol.18 (11), p.3769
Main Authors: Jorquera Valero, José María, Sánchez Sánchez, Pedro Miguel, Fernández Maimó, Lorenzo, Huertas Celdrán, Alberto, Arjona Fernández, Marcos, De Los Santos Vílchez, Sergio, Martínez Pérez, Gregorio
Format: Article
Language:English
Subjects:
adaptability
Algorithms
anomaly detection
applications
Area Under Curve
Cell Phone
Computer Security
continuous authentication
cybersecurity
Electric Power Supplies
Humans
machine learning
mobile devices
sensors
Support Vector Machine
Time Factors
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Summary:Continuous authentication systems for mobile devices focus on identifying users according to their behaviour patterns when they interact with mobile devices. Among the benefits provided by these systems, we highlight the enhancement of the system security, having permanently authenticated the users; and the improvement of the users' quality of experience, minimising the use of authentication credentials. Despite the benefits of these systems, they also have open challenges such as the authentication accuracy and the adaptability to new users' behaviours. Continuous authentication systems should manage these challenges without forgetting critical aspects of mobile devices such as battery consumption, computational limitations and response time. With the goal of improving these previous challenges, the main contribution of this paper is the design and implementation of an intelligent and adaptive continuous authentication system for mobile devices. The proposed system enables the real-time users' authentication by considering statistical information from applications, sensors and Machine Learning techniques based on anomaly detection. Several experiments demonstrated the accuracy, adaptability, and resources consumption of our solution. Finally, its utility is validated through the design and implementation of an online bank application as proof of concept, which allows users to perform different actions according to their authentication level.
ISSN:1424-8220
1424-8220
DOI:10.3390/s18113769