DroidFusion: A Novel Multilevel Classifier Fusion Approach for Android Malware Detection

Suleiman Y. Yerima, Sakir Sezer

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

Android malware has continued to grow in volume and complexity posing significant threats to the security of mobile devices and the services they enable. This has prompted increasing interest in employing machine learning to improve Android malware detection. In this paper, we present a novel classifier fusion approach based on a multilevel architecture that enables effective combination of machine learning algorithms for improved accuracy. The framework (called DroidFusion), generates a model by training base classifiers at a lower level and then applies a set of ranking-based algorithms on their predictive accuracies at the higher level in order to derive a final classifier. The induced multilevel DroidFusion model can then be utilized as an improved accuracy predictor for Android malware detection. We present experimental results on four separate datasets to demonstrate the effectiveness of our proposed approach. Furthermore, we demonstrate that the DroidFusion method can also effectively enable the fusion of ensemble learning algorithms for improved accuracy. Finally, we show that the prediction accuracy of DroidFusion, despite only utilizing a computational approach in the higher level, can outperform stacked generalization, a well-known classifier fusion method that employs a meta-classifier approach in its higher level.
Original languageEnglish
Number of pages14
JournalIEEE Transactions on Cybernetics
DOIs
Publication statusPublished - 03 Jan 2018

Keywords

  • Malware
  • Android
  • Humanoid Robots
  • feature extraction
  • Support Vector Machines
  • Decision Trees
  • Machine Learing Algorithms
  • Android malware
  • classifier fusion
  • ensemble learning
  • mobile security
  • stacked generalization

ASJC Scopus subject areas

  • Computer Science (miscellaneous)

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