A Study of Permission-based Malware Detection Using Machine Learning

Abstract

Malware is becoming more prevalent, and several threat categories have risen dramatically in recent years. This paper provides a bird's-eye view of the world of malware analysis. It also presents a brief review of malware analysis approaches, common detection types, and some basic preventive strategies from various angles. An experiment has been done to show the influence of human factors on people. This study shows that most people are more likely to fall victim to a malware attack if that seems to come from a reliable source or person. The efficiency of five different machine learning methods (Naive Bayes, K-Nearest Neighbor, Decision Tree, Random Forest, Decision Forest) combined with features picked from the retrieval of Android permissions to categorize applications as harmful or benign is investigated in this study. On a test set consisting of 1,168 samples (each consisting of 948 features), produce accuracy rates above 80% (Except Naive Bayes Algorithm with 65% accuracy). Of the considered algorithms TensorFlow Decision Forest performed the best with an accuracy of 90%.