EKMPRFG: Ensemble of KNN, Multilayer Perceptron and Random Forest using Grading for Android Malware Classification
Niranjan A.1, K. S. S. S. Abhishikth2, P. Deepa Shenoy3, Venugopal K. R.4
1Niranjan A., Research Scholar, Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, Bangalore, India
2K. S. S. S. Abhishikth, Department of Computer Science, University of South Florida, USA.
3P. Deepa Shenoy, Professor, Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, Bangalore, India.
4Venugopal K. R., Vice Chancellor, Bangalore University, Bangalore, India.
Manuscript received on January 01, 2020. | Revised Manuscript received on January 20, 2020. | Manuscript published on January 30, 2020. | PP: 3353-3360 | Volume-8 Issue-5, January 2020. | Retrieval Number: E5866018520/2020©BEIESP | DOI: 10.35940/ijrte.E5866.018520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Android is the most popular Operating Systems with over 2.5 billion devices across the globe. The popularity of this OS has unfortunately made the devices and the services they enable, vulnerable to numerous security threats. As a result of this, a significant research is being done in the field of Android Malware Detection employing Machine Learning Algorithms. Our current work emphasizes on the possible use of Machine Learning techniques for the detection of malware on such android devices. The proposed EKMPRFG is applied for the classification of Android Malware after a preprocessing phase involving a hybrid Feature Selection model using proposed Standard Deviation of Standard Deviation of Ranks (SDSDR) and several other builtin Feature Selection algorithms such as Correlation based Feature Selection (CFS), Classifier SubsetEval, Consistency SubsetEval, and Filtered SubsetEval followed by Principal Component Analysis(PCA) for dimensionality reduction. The experimental results obtained on two data sets indicate that EKMPRFG outperforms the existing works in terms of Prediction Accuracy and Weighted F- Measure values.
Keywords: Ensemble Learning, Hybrid Feature Selection, Malware Classification, Malware Analysis.
Scope of the Article: Classification.