Creating Usage Models to Identify Misbehaving Applications on Mobile Devices
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Date
2019-08-16
Authors
Jiang, Qiushi
Advisor
Ward, Paul
Journal Title
Journal ISSN
Volume Title
Publisher
University of Waterloo
Abstract
Limited battery capacity is currently a major pain point for mobile users. The problem
is made worse when poorly designed applications consume a significant amount of power in
the background when they are not actively used by the user. To combat this problem, we
propose an automated monitoring system that can detect misbehaving applications running
on mobile devices. Our system does not require any prior knowledge about the monitored
applications. Instead, it collects the user’s usage records and builds models to encapsulate
the contexts when the user is likely to use each application. From those models, our
system can identify misbehaving applications that are consuming system resources while
providing no useful service to the end user. In this dissertation, we demonstrate the overall
design for our system. This design allows us to collect detailed usage records while keeping
our system’s power consumption at a minimum. We also introduce the steps we take to
construct our usage models and the rationale behind each key decisions. In the end, we
evaluate the effectiveness of our system by running it on a real Android device during a
two month period. From the experiment, we show the misbehaving applications identified
by our system have a significant impact on the battery life, and misbehaving applications
with high network usage is the main cause of fast battery drain.
Description
Keywords
Automated monitoring, user modeling, context-based computing, data analytics, Android, battery, mobile device