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VIRUSMETER: PREVENTING YOUR CELLPHONE FROM SPIESRAID 2009
Lei Liu, Department of Computer Science, George Mason UniversityGuanhua Yan, Information Sciences Group, Los Alamos National LaboratoryXinwen Zhang, Computer Science Lab, Samsung Information Systems AmericaSongqing Chen, Department of Computer Science, George Mason University
Introduction
1 billion camera phones to be shipped in 2008 Smartphones: about 10%, 100 million
units By the end of 2007, over 370
different mobile malware Information stealing, overcharging,
battery exhaustion, network congestion
Introduction
Signature-based Encryption, obfuscation, packing
Anomaly-based High false alarm rate
Behavioral signatures Resource-constrained FlexiSPY-like malware doesn’t show
anomalies in the order of relevant API calls
Introduction
VirusMeter Based on battery power
Challenges Require power model Need to measure battery power in real-
time Lightweight. Cannot consume too much
CPU and power
Related Work
Infection vectors Bluetooth, MMS, memory cards, user
downloading Epidemic spreading in mobile, 2005
ACM WiSe Use user interaction to identify
vulnerable users, 2006 ACM WiSe Behavioral signatures for mobile mal
ware detection, 2008 Mobisys
Related Work
Limit Targeting particular situations (e.g.,
attack through MMS) Demand significant infrastructure
support Demand non-trivial computing resoures
from mobile devices