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App-Collusion Detection Using a Two-Stage Classifier

  • Md. Faiz Iqbal FaizEmail author
  • Md. Anwar Hussain
  • Ningrinla Marchang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 927)

Abstract

Various single app analysis tools are developed for checking Android malwares in the smartphones. All of them are unable to detect threats caused by more than one app. Android app-collusion is one such threat. App-collusion is a scenario where two or more apps collaborate with each other to achieve a malicious goal. This paper presents an approach for detecting collusive app-pairs using machine learning. The proposed approach works in two stages. In the fist stage, we train a base classifier using a set of benign and malicious applications. In the second stage, we use the parameter vector from the first stage and a different classifier to detect collusive app-pairs. We achieve detection rates of 90% and 87.5% on two sets of colluding app-pairs.

Notes

Acknowledgements

This work was funded by grant from the Visvesvaraya Fellowship Scheme (Govt. of India).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Md. Faiz Iqbal Faiz
    • 1
    Email author
  • Md. Anwar Hussain
    • 1
  • Ningrinla Marchang
    • 1
  1. 1.North Eastern Regional Institute of Science and TechnologyItanagarIndia

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