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Erschienen in:
Ensemble Learning for Low-Level Hardware-Supported Malware Detection Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

Continuous Authentication on Mobile Devices Using Power Consumption, Touch Gestures and Physical Movement of Users

verfasst von : Rahul Murmuria, Angelos Stavrou, Daniel Barbará, Dan Fleck

Erschienen in: Research in Attacks, Intrusions, and Defenses

Verlag: Springer International Publishing

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Abstract

Handheld devices today do not continuously verify the identity of the user while sensitive activities are performed. This enables attackers, who can either compromise the initial password or grab the device after login, full access to sensitive data and applications on the device. To mitigate this risk, we propose continuous user monitoring using a machine learning based approach comprising of an ensemble of three distinct modalities: power consumption, touch gestures, and physical movement. Users perform different activities on different applications: we consider application context when we model user behavior. We employ anomaly detection algorithms for each modality and place a bound on the fraction of anomalous events that can be considered “normal” for any given user. We evaluated our system using data collected from 73 volunteer participants. We were able to verify that our system is functional in real-time while the end-user was utilizing popular mobile applications.

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Metadaten
Titel
Continuous Authentication on Mobile Devices Using Power Consumption, Touch Gestures and Physical Movement of Users
verfasst von
Rahul Murmuria
Angelos Stavrou
Daniel Barbará
Dan Fleck
Copyright-Jahr
2015
Buch
Research in Attacks, Intrusions, and Defenses

Print ISBN: 978-3-319-26361-8

Electronic ISBN: 978-3-319-26362-5

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-26362-5_19

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