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VirusMeter: Preventing Your Cellphone from Spies

  • Lei Liu
  • Guanhua Yan
  • Xinwen Zhang
  • Songqing Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5758)

Abstract

Due to the rapid advancement of mobile communication technology, mobile devices nowadays can support a variety of data services that are not traditionally available. With the growing popularity of mobile devices in the last few years, attacks targeting them are also surging. Existing mobile malware detection techniques, which are often borrowed from solutions to Internet malware detection, do not perform as effectively due to the limited computing resources on mobile devices.

In this paper, we propose VirusMeter, a novel and general malware detection method, to detect anomalous behaviors on mobile devices. The rationale underlying VirusMeter is the fact that mobile devices are usually battery powered and any malicious activity would inevitably consume some battery power. By monitoring power consumption on a mobile device, VirusMeter catches misbehaviors that lead to abnormal power consumption. For this purpose, VirusMeter relies on a concise user-centric power model that characterizes power consumption of common user behaviors. In a real-time mode, VirusMeter can perform fast malware detection with trivial runtime overhead. When the battery is charging (referred to as a battery-charging mode), VirusMeter applies more sophisticated machine learning techniques to further improve the detection accuracy. To demonstrate its feasibility and effectiveness, we have implemented a VirusMeter prototype on Nokia 5500 Sport and used it to evaluate some real cellphone malware, including FlexiSPY and Cabir. Our experimental results show that VirusMeter can effectively detect these malware activities with less than 1.5% additional power consumption in real time.

Keywords

mobile malware mobile device security anomaly detection power consumption 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Lei Liu
    • 1
  • Guanhua Yan
    • 2
  • Xinwen Zhang
    • 3
  • Songqing Chen
    • 1
  1. 1.Department of Computer ScienceGeorge Mason UniversityUSA
  2. 2.Information Sciences Group (CCS-3)Los Alamos National LaboratoryUSA
  3. 3.Computer Science LabSamsung Information Systems AmericaUSA

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