VISUALISASI SERANGAN MALWARE SPYWARE MENGGUNAKAN METODE K-MEANS CLUSTERING

SAIFULLAH, MUHAMMAD ARIEF and Stiawan, Deris (2023) VISUALISASI SERANGAN MALWARE SPYWARE MENGGUNAKAN METODE K-MEANS CLUSTERING. Undergraduate thesis, Sriwijaya University.

	Text RAMA_56201_09011381924129.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (3MB) | Request a copy
	Text RAMA_56201_09011381924129_0003047905_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (5MB) | Request a copy
	Text RAMA_56201_09011381924129_0003047905_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (876kB)
	Text RAMA_56201_09011381924129_0003047905_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (432kB) | Request a copy
	Text RAMA_56201_09011381924129_0003047905_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (585kB) | Request a copy
	Text RAMA_56201_09011381924129_0003047905_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) | Request a copy
	Text RAMA_56201_09011381924129_0003047905_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (7kB) | Request a copy
	Text RAMA_56201_09011381924129_0003047905_06_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (85kB) | Request a copy
	Text RAMA_56201_09011381924129_0003047905_07_Lamp.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (890kB) | Request a copy

Abstract

K-means clustering is a tool for determining the cluster structure of a data set identified by its strong similarity to other clusters or its strong differences from other clusters. Another article says that the working method of the K-Means algorithm requires using centroids as cluster prototypes and previous cluster results as output. The dataset comes from CIC-MalMem2022 provided by UNB CIC. The dataset provided by CIC-MalMem2022 has balanced data, so no data balancing process is required. K-Means managed to group 2 clusters with a silhouette score of 0.6. For best validation results, use K-Means labels using the logistic regression model on 5-Fold. The accuracy of using the K-Means label is 99.95%, so the K-Means label is better than using the malware-spyware label, which is only 99.36%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	METODE K-MEANS CLUSTERING
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.A25 Computer security. Systems and Data Security.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Muhammad Arief Saifullah
Date Deposited:	22 Nov 2023 08:30
Last Modified:	23 Nov 2023 04:53
URI:	http://repository.unsri.ac.id/id/eprint/130883

Actions (login required)

View Item