KLASIFIKASI SERANGAN SPYWARE DENGAN MENGGUNAKAN METODE K-NEAREST NEIGHBORS (KNN)

PEDERSON, MULKI and Stiawan, Deris (2024) KLASIFIKASI SERANGAN SPYWARE DENGAN MENGGUNAKAN METODE K-NEAREST NEIGHBORS (KNN). Undergraduate thesis, Sriwijaya University.

	Text RAMA_56201_09011282025086.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (3MB) | Request a copy
	Text RAMA_56201_09011282025086_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (4MB) | Request a copy
	Text RAMA_56201_09011282025086_0003047905_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (2MB)
	Text RAMA_56201_09011282025086_0003047905_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (265kB) | Request a copy
	Text RAMA_56201_09011282025086_0003047905_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (475kB) | Request a copy
	Text RAMA_56201_09011282025086_0003047905_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (992kB) | Request a copy
	Text RAMA_56201_09011282025086_0003047905_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (26kB) | Request a copy
	Text RAMA_56201_09011282025086_0003047905_06_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (83kB) | Request a copy
	Text RAMA_56201_09011282025086_0003047905_07_lamp.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (389kB) | Request a copy

Abstract

Spyware is one type of malware that threatens computer systems because it can steal users' personal information and sensitive data without their knowledge. Spyware can monitor user activities and steal data such as visited websites, email addresses, and even record keyboard and screen activities. This research aims to classify spyware attacks using the K-Nearest Neighbors (KNN) algorithm. The research dataset consists of spyware malware data and benign data available in the CICMalMem2022 dataset. In this study, data splitting is performed with Stratified K-Fold to obtain the optimal number of folds that can achieve more optimal classification results for each parameter k.The research results indicate that the KNN algorithm is highly accurate in classifying spyware attack data, achieving the highest results with 20 best features using k=3 and fold=4, reaching an accuracy of 99.91%. With such results, it can be said that the use of the KNN algorithm is effective in identifying spyware attacks with a high level of accuracy.. With these results, it can be said that the use of the KNN algorithm is effective in identifying spyware attacks with a high level of accuracy.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Klasifikasi Malware, Spyware, K-Nearest Neighbors, Stratified K-Fold.
Subjects:	T Technology > T Technology (General) > T57.6-57.97 Operations research. Systems analysis > T57.85 Network systems theory Including network analysis Cf. TS157.5+ Scheduling T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7885-7895 Computer engineering. Computer hardware Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150-4380 Computer network resources
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Mulki Pederson
Date Deposited:	12 Jun 2024 07:54
Last Modified:	12 Jun 2024 07:54
URI:	http://repository.unsri.ac.id/id/eprint/146770

Actions (login required)

View Item