VISUALISASI SERANGAN MALWARE BOTNET MENGGUNAKAN METODE CLUSTERING K-MEANS

SAFITRI, NAZULA RAHMA and Setiawan, Deris (2021) VISUALISASI SERANGAN MALWARE BOTNET MENGGUNAKAN METODE CLUSTERING K-MEANS. Undergraduate thesis, Sriwijaya University.

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Abstract

Malware Attack Visualization aims to make it easier to recognize the type of malware and normal data. Malware or malicious software is a code or program file that is usually sent over the internet, to steal, infect, or perform some other dangerous operating system. While the Botnet is a network of devices infected by malicious software and controlled by an external operator called a botmaster. The purpose of this study is to get the best level of accuracy in Botnet Malware Attack Visualization using clustering method K-Means by using dataset namely MedBIoT project. The extraction feature in this study uses CICFlowMeters tools from the University of New Brunswick (UNB). In this study also used feature selection extra-tree classifier that aims to choose the best feature. The visualization results using clustering method K-Means showed a fairly good result which is an accuracy value of 99.17% which indicates accuracy in visualizing botnet malware attacks in this study.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Visualisasi, Malware Botnet, CICFlowMeter, Extra Tree Classifier, Clustering K-Means
Subjects:	Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.A25 Computer security. Systems and Data Security. Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.E94 Computer system performance. Computer Communication Networks. Computer science. Logic design. Operating systems (Computers).
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Nuzula Rahma Safitri
Date Deposited:	08 Sep 2021 02:00
Last Modified:	08 Sep 2021 02:00
URI:	http://repository.unsri.ac.id/id/eprint/53005

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