IMPLEMENTASI PRINCIPAL COMPONENT ANALYSIS (PCA) DAN ALGORITMA NAÏVE BAYES CLASSIFIER PADA KLASIFIKASI BOTNET DI JARINGAN INTERNET OF THINGS (IoT)

APRIANTI, WIDYANA and Deris Stiawan. M.T., Ph.D, Deris (2021) IMPLEMENTASI PRINCIPAL COMPONENT ANALYSIS (PCA) DAN ALGORITMA NAÏVE BAYES CLASSIFIER PADA KLASIFIKASI BOTNET DI JARINGAN INTERNET OF THINGS (IoT). Undergraduate thesis, Sriwijaya University.

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Abstract

A botnet is a program that destroys a network system and spreads to all computers via the internet. The bot performs actions such as sending a request to the target website by turning the site into an attack. In this study using the MedBIoT dataset with three types of botnets, namely mirai, torii, and bashlite. Principal Component Analysis (PCA) is used to reduce dimensional data by creating a small number of new variables. The Naïve Baye Classifier algorithm consists of two models, namely the Bernoulli and Gaussian models. The results of this study prove that the Naïve Baye Classifier algorithm using Principal Component Analysis (PCA) can perform the botnet classification process well. The results of the classification using the Gaussian model as the best result with an accuracy value of 97.71%, precision 96.90%, and recall 97.49%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Botnet Classification, MedBIoT, Principal Component Analysis (PCA), Naïve Baye Classifier
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7885-7895 Computer engineering. Computer hardware
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Widyana Aprianti
Date Deposited:	29 Jul 2021 08:14
Last Modified:	29 Jul 2021 08:14
URI:	http://repository.unsri.ac.id/id/eprint/50970

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