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

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Penilaian

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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.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2407003737	T146770	T1467702024	Central Library (REFERENCES)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1467702024

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer., 2024

Collation

xiii, 59 hlm.; ilus.; tab.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.840 7

Content Type

Text

Media Type

unmediated

Carrier Type

unspecified

Edition

-

Subject(s)

Prodi Sistem Komputer
Klasifikasi Malware

Specific Detail Info

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Statement of Responsibility

UIN Farrah

Other version/related

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