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Mobile App and Malware Classifications by Mobile Usage with Time Dynamics

  • Yong ZhengEmail author
  • Sridhar Srinivasan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 926)

Abstract

Smartphones have become a popular target for cyberattacks. Malware can be embedded into the mobile applications. Several techniques have been proposed to alleviate these problems. However, these solutions may perform experiments by using simulated data, or may require root system privileges, or did not take advantage of the discovered patterns to build more effective malware detection methods. In this paper, we use the SherLock data which is a labeled smartphone dataset that captures ongoing attacks within the low-privileged monitorable features. We analyze the usage behaviors, discover temporal and usage patterns, and further examine multiple classification techniques to predict the type and the running state (i.e., benign and malicious) of the mobile apps by using different combinations of feature sets. Our experiments identified the best feature sets and methods to detect malwares, and we demonstrate the usefulness of temporal information in the predictive analysis.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Illinois Institute of TechnologyChicagoUSA

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