Anomaly Detection for Mobile Device Comfort

In this work, we utilize the data that can be collected via some of the sensors found on modern smartphones to find anomalies in the behaviour of a smartphone user and the current context. This can be done to prevent the mobile phone from being compromised physically or to warn the user when he/she is behaving "unusually."We apply the "time slice" notion to existing anomaly detection methods, evaluate our approach on two published data sets, and confirm that it is feasible to use our approach on smartphones with modest hardware.Our work is part of Marsh et al.'s Device Comfort paradigm, which is an application of computational trust to mobile device security, where the mobile device is able to have a varying level of "trust" in its user based on behavioural biometrics, and the contexts in which the user interacts with the mobile device.

Name thesis:degree_name

Master of Applied Science (M.App.Sc.)

Level thesis:degree_level

Master's

Discipline thesis:degree_discipline

Engineering, Electrical and Computer

Grantor dc:publisher

Carleton University

Year dc:date.issued

2013

Author dc:creator

• Bicakci, Mehmet Vefa

Statement dc:rights

• Copyright © 2013 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.

Language dc:language.iso

en

DOI

10.22215/etd/2013-09949

OAI identifier oai:identifier

oai:carleton.scholaris.ca:20.500.14718/32764