KLASIFIKASI MALWARE BANKING PADA ANDROID DENGAN METODE SUPPORT VECTOR MACHINE

OCTAFIAN, OCTAFIAN and Stiawan, Deris and Heryanto, Ahmad (2021) KLASIFIKASI MALWARE BANKING PADA ANDROID DENGAN METODE SUPPORT VECTOR MACHINE. Undergraduate thesis, Sriwijaya University.

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Abstract

Malware classification is a way to recognize types of data that are classified as malware or normal files. Banking Malware is a type of trojan that aims to deceive bank customers and financial institutions, allowing victims to transfer funds from the victim's account to the attacker's account. The purpose of this study is to obtain the best level of accuracy in the classification of Banking Malware using a support vector machine method using a dataset from the University of New Brunswick, namely the CICMALDROID2020. The extraction feature in the study uses the CICFlowMeters tool to convert files into ready-to-process files. This research also uses a feature selection extra-tree classifier which aims to select the best features. The results of the classification using the support vector machine method show fairly good results, namely an accuracy value of 87% which indicates the accuracy in the classification of banking malware attacks in this study.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Klasifikasi, Banking Malware, CICFlowMeters, Extra tree Classifier, Support Vector Machine
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 5608 not found.
Date Deposited:	07 Jun 2021 04:26
Last Modified:	07 Jun 2021 04:26
URI:	http://repository.unsri.ac.id/id/eprint/47745

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