COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR THE DETECTION OF ANDROID MALWARE

Abstract

This paper examines the effectiveness of some machine learning algorithms in the detection of android malicious application. In order to carry out this analysis, drebin dataset of android malicious and good applications were obtained and used for the classification as described in a section of this article. The classification results show that the Cubic SVM, Quadratic SVM and ensemble Subspace KNN performed better with 99.2%, 98.7% and 98.4% accuracy with 0.0079, 0.0129 and 0.1598 error rate respectively.