Static Code Analysis of Permission-based Features for Android Malware Classification Using Apriori Algorithm with Particle Swarm Optimization

Abstract

Several machine learning techniques based on supervised learning have been applied to classify malware. However, supervised learning technique has limitations for malware classification task. This paper presents a classification approach on android malware using candidate detectors generated from an unsupervised association rule of Apriori Algorithm. The algorithm is improved with Particle Swarm Optimization that trains three different supervised classifiers. In this method, permission-based features were extracted from Android applications byte-code through static code analysis, selected and were used to train supervised classifiers. Using a number of candidate detectors from an improved Apriori Algorithm with Particle Swarm Optimization, the true positive rate of detecting malicious code is maximized, while the false positive rate of wrongful detection is minimized. The results of the experiments show that the proposed combined technique has better results as compared to using only supervised or unsupervised learners.