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Sensors (Basel). 2018 Nov 4;18(11). pii: E3769. doi: 10.3390/s18113769.

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

Author information

1
Department of Information and Communications Engineering (DIIC), University of Murcia, 30100 Murcia, Spain. josemaria.jorquera@um.es.
2
Department of Information and Communications Engineering (DIIC), University of Murcia, 30100 Murcia, Spain. pedromiguel.sanchez@um.es.
3
Department of Computer Engineering (DITEC), University of Murcia, 30100 Murcia, Spain. lfmaimo@um.es.
4
Telecommunications Software & Systems Group, Waterford Institute of Technology, Co., X91 K0EK Waterford, Ireland. ahuertas@tssg.org.
5
Innovation and Labs, ElevenPaths, Cybersecurity Unit of Telefónica Digital España, 29071 Málaga, Spain. marcos.arjona@11paths.com.
6
Innovation and Labs, ElevenPaths, Cybersecurity Unit of Telefónica Digital España, 29071 Málaga, Spain. ssantos@11paths.com.
7
Department of Information and Communications Engineering (DIIC), University of Murcia, 30100 Murcia, Spain. gregorio@um.es.

Abstract

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.

KEYWORDS:

adaptability; anomaly detection; applications; continuous authentication; cybersecurity; machine learning; mobile devices; sensors

PMID:
30400377
PMCID:
PMC6263905
DOI:
10.3390/s18113769
[Indexed for MEDLINE]
Free PMC Article

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