An Efficient Android Malware Detection Framework with Stacking Ensemble Model

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2022 by IJETT Journal
Volume-70 Issue-4
Year of Publication : 2022
Authors : A. Lakshmanarao, M. Shashi
  10.14445/22315381/IJETT-V70I4P226

MLA 

MLA Style: Lakshmanarao, A., and Shashi, M. "An Efficient Android Malware Detection Framework with Stacking Ensemble Model." International Journal of Engineering Trends and Technology, vol. 70, no. 4, Apr. 2022, pp. 294-302. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I4P226

APA Style: Lakshmanarao, A., & Shashi, M.(2022). An Efficient Android Malware Detection Framework with Stacking Ensemble Model. International Journal of Engineering Trends and Technology, 70(4), 294-302. https://doi.org/10.14445/22315381/IJETT-V70I4P226

Abstract
Due to the increased frequency of cyber-attacks with various targeted objectives, cyber security has become a major concern for society. Android phones being the most widely used devices, they are targeted in most of the attacks with malware. So, it is vital to explore innovative ways of identifying Android Malware attacks. Machine learning and deep learning have been employed to develop classifiers to determine if an app is malware or benign. Android apps are represented by a set of attributes that can describe their behaviour. This paper proposes a stacking ensemble model for detecting Android malware. The proposed framework is designed with two variants of stacking ensemble: blending and stacking. The dex files of android apps are extracted and translated into images. Later, a stacking ensemble is applied to the image dataset. Convolutional Neural Networks are used as base learners, and a Support Vector Machine is used as a meta learner. The experimental results of modelling with blending and stacking showed 99% and 98.3% accuracy, which advocates support of the proposed framework for Android malware detection.

Keywords
Android malware detection, CNN, Stacking Ensemble, SVM.

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