Analysis of Machine learning classifiers for Android Malware Detection

In the current scenario, android users utilise their devices for day-to-day tasks, they commonly use internet banking and online shopping. This possesses a greater threat to the android devices. Android malware continues to emerge at an alarming rate, necessitating the development of increasingly effective detection tools. This chapter aims to analyse different machine learning classifiers for android malware detection. An experiment has been performed on the CICMalDroid 2020 dataset, consisting of more than 17,341 recent and sophisticated android samples. After preprocessing the data, classifiers are applied. Later the results are elucidated concerning accuracy and prediction performance. Results were comparatively analysed. In this paper, focus is to choose the best classifiers available in Weka for the dataset under consideration. Therefore the proposed work constitutes an exciting step forward to help users understand the best machine learning classifier for a specific dataset.