Abstract
Modern mobile devices come with an array of sensors that support many interesting applications. However, sensors have different sampling costs (e.g., battery drain) and benefits (e.g., accuracy) under different circumstances. In this work we investigate the trade-off between the cost of using a sensor and the benefit gained from its use, with application to data-driven authentication on mobile devices. Current authentication practice, where user behaviour is first learned from the sensor data and then used to detect anomalies, typically assumes a fixed sampling rate and does not consider the battery consumption and usefulness of sensors. In this work we study how battery consumption and sensor effectiveness (e.g., for detecting attacks) vary when using different sensors and different sensor sampling rates. We use data from both controlled lab studies, as well as field trials, for our experiments. We also propose an adaptive sampling technique that adjusts the sampling rate based on an expected device vigilance level. Our results show that it is possible to reduce the battery consumption tenfold without significantly impacting the detection of attacks.
Original language | English |
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Title of host publication | 2015 IEEE International Conference on Pervasive Computing and Communications (PerCom) |
Publisher | IEEE |
Pages | 189-197 |
Number of pages | 9 |
ISBN (Print) | 9781479980345, 9781479980338 |
DOIs | |
Publication status | Published - 1 Jul 2015 |
Event | 2015 IEEE International Conference on Pervasive Computing and Communications (PerCom) - St. Louis, MO, United States Duration: 23 Mar 2015 → 27 Mar 2015 |
Conference
Conference | 2015 IEEE International Conference on Pervasive Computing and Communications (PerCom) |
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Abbreviated title | 2015 PerCom |
Country/Territory | United States |
City | St. Louis, MO |
Period | 23/03/15 → 27/03/15 |
ASJC Scopus subject areas
- Computer Science Applications
- Computer Networks and Communications
- Software
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Mike Just
- School of Mathematical & Computer Sciences - Associate Professor
- School of Mathematical & Computer Sciences, Computer Science - Associate Professor
Person: Academic (Research & Teaching)