VISUALISASI POLA SERANGAN BRUTE FORCE MENGGUNAKAN METODE K-NEAREST NEIGHBOR

BAHARI, MUHAMMAD ROBBY and Heryanto, Ahmad and Hermansyah, Adi (2022) VISUALISASI POLA SERANGAN BRUTE FORCE MENGGUNAKAN METODE K-NEAREST NEIGHBOR. Undergraduate thesis, Sriwijaya University.

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Abstract

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.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Brute Force, Machine Learning, K-Nearest Neighbor, Feature Selection
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:	Mr. Muhammad Robby Bahari
Date Deposited:	05 Jul 2022 02:29
Last Modified:	05 Jul 2022 02:29
URI:	http://repository.unsri.ac.id/id/eprint/73305

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