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VISUALISASI POLA SERANGAN BRUTE FORCE MENGGUNAKAN METODE K-NEAREST NEIGHBOR

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Penilaian

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Brute Force is one of the most frequently used methods by hackers in cyber crimes. To find out which variable features have the most significant role in the brute force dataset, it is necessary to implement feature selection. This final project discusses the visualization of brute force attack patterns using several feature selection methods, namely Random Forest Classifier (RFC), Mutual Information Classifier (MIC), Correlation Based Selection (CBS), and also Lasso Regularization Regression (LRR) and then classification using K-Nearest Neighbor algorithm to determine accuracy, precision, recall, and also F1-score. The data used in this study is CIC-IDS 2017 which is sourced from the Canadian Institute Cybersecurity. From the research conducted, it is found that the Random Forest Classifier feature selection produces the best accuracy, precision, recall, and F1-score among the others.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2207002006	T73305	T733052022	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T733052022

Publisher

Inderalaya : Jurusan Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2022

Collation

xvi, 87 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

006.307

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Sistem Pakar
Jurusan Sistem Komputer

Specific Detail Info

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Statement of Responsibility

SEPTA

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