Performance Comparison of Android Malware Detection Methods

Abstract

Abstract With the growth of Android apps market share, there are more and more kinds of malicious apps for Android system. At the same time, the method for malware detecting is evolving. In order to deal with the growing threat of malware, many detecting methods are combined with machine learning algorithms. In this paper, we compared the ability to detect malware of five algorithms on a dataset about Android apps networking. The result revealed that the networking data could be used as a reference for the classification of Android apps. Moreover, among the five algorithms used in this paper, the ensemble learning algorithm using the decision tree as base leaner performed the best in the comparison. It reached more than 75% accuracy when predicting malware using the test set.