Android smartphones is widely used for banking transactions. Thus, it can be at risk of malware attacks. Malware classification is a method that serves to identify and distinguish types of data classified as malware or normal. Banking Malware is malware designed to gain access to user's online banking accounts by impersonating a real banking application or web banking interface. This study aims to obtain the best level of accuracy in the classification of Banking Malware using the random forest algorithm with a dataset originating from the University of New Brunswick, namely CICMALDROID2020. The extraction feature used is the CICFlowMeters tool to process a dataset from a PCAP file into a CSV file. This research also use feature selection boruta which functions to select the best features in the dataset. The classification results using the random forest algorithm are evaluated using a confusion matrix. The highest accuracy obtained in this study was 92.5%, with a precision value of 93.28% and a recall of 93.73%.
Authors: Ahmad Aji Guntur Saputra, Deris Stiawan, Ahmad Heryanto
Publish Year: 2021
