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IntelliAV: Toward the Feasibility of Building Intelligent Anti-malware on Android Devices

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10410)

Abstract

Android is targeted the most by malware coders as the number of Android users is increasing. Although there are many Android anti-malware solutions available in the market, almost all of them are based on malware signatures, and more advanced solutions based on machine learning techniques are not deemed to be practical for the limited computational resources of mobile devices. In this paper we aim to show not only that the computational resources of consumer mobile devices allow deploying an efficient anti-malware solution based on machine learning techniques, but also that such a tool provides an effective defense against novel malware, for which signatures are not yet available. To this end, we first propose the extraction of a set of lightweight yet effective features from Android applications. Then, we embed these features in a vector space, and use a pre-trained machine learning model on the device for detecting malicious applications. We show that without resorting to any signatures, and relying only on a training phase involving a reasonable set of samples, the proposed system outperforms many commercial anti-malware products, as well as providing slightly better performances than the most effective commercial products.

Keywords

Android Malware detection Machine learning On-device TensorFlow Mobile security Classification 

Notes

Acknowledgement

We appreciate VirusTotal’s collaboration for providing us the access to a large set of Android applications.

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Copyright information

© IFIP International Federation for Information Processing 2017

Authors and Affiliations

  • Mansour Ahmadi
    • 1
  • Angelo Sotgiu
    • 1
  • Giorgio Giacinto
    • 1
  1. 1.University of CagliariCagliariItaly

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