KLASIFIKASI MALWARE BANKING PADA ANDROID MENGGUNAKAN ALGORITMA RANDOM FOREST

SAPUTRA, AHMAD AJI GUNTUR and Stiawan, Deris and Heryanto, Ahmad (2021) KLASIFIKASI MALWARE BANKING PADA ANDROID MENGGUNAKAN ALGORITMA RANDOM FOREST. Undergraduate thesis, Sriwijaya University.

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Abstract

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%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Klasifikasi, Banking Malware, CICFlowMeters, Boruta, Random Forest
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7885-7895 Computer engineering. Computer hardware
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Users 5591 not found.
Date Deposited:	07 Jun 2021 05:48
Last Modified:	07 Jun 2021 05:48
URI:	http://repository.unsri.ac.id/id/eprint/47743

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