Lei Liu, Department of Computer Science, George Mason University Guanhua Yan, Information Sciences Group, Los Alamos National Laboratory Xinwen Zhang, Computer Science Lab, Samsung Information Systems America Songqing Chen, Department of Computer Science, George Mason University

Outline  Introduction  Related Work  Overview

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 malware detection, 2008 Mobisys Behavioral signatures for mobile malware detection

Related Work  Limit  Targeting particular situations (e.g., attack through MMS)  Demand significant infrastructure support  Demand non-trivial computing resoures from mobile devices

Overview