Malicious software aims to destroy,disable,or control computer systems.Android malware is specifically targeted at the Android operating system,with the aim of leaking confidential information and damaging the system.The literature shows that multiple attempts have been made in the relevant field to detect Android malware.However,these tasks cannot automatically detect malware,and most of them are signature-based,making it impossible to detect new variants of malware.In this study,different algorithms were explored to obtain the best algorithm for predicting malware and to obtain the best feature set that can help effectively predict malware.The analysis of this study shows that ensemble methods are better than traditional machine learning algorithms in predicting malware.The LGBM innovative algorithm is used in this study to reduce the number of features from 215 to 100,with an accuracy rate of 99.5％.In addition,an accuracy of 99.17％is achieved using a random forest with only 55 features.
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