VISUALISASI DATA SERANGAN UDP FLOOD PADA JARINGAN INTERNET OF THINGS (IOT) MENGGUNAKAN ALGORITMA NAIVE BAYES CLASSIFIER

MARLO, CHRISTOPER and Stiawan, Deris (2022) VISUALISASI DATA SERANGAN UDP FLOOD PADA JARINGAN INTERNET OF THINGS (IOT) MENGGUNAKAN ALGORITMA NAIVE BAYES CLASSIFIER. Undergraduate thesis, Sriwijaya University.

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Abstract

This study focuses on visualize the UDP Flood Attack Pattern on Internet of Things Network Dataset ( doi.org/10.528 /zenodo.4436127). The purpose of this study is to obtain a visual form of the dataset classification results using the Naive Bayes Classifier algorithm which shows the difference between UDP Flood attack data and normal data. The method used in this study is the Naive Bayes Classifier as a classifier that applied to datasets. The results from this study showed that the classification carried out using the Naive Bayes Classifier algorithm obtained a classification accuracy rate of 99.80% using the Multinominal Naive Bayes model which applied to the dataset. The classification results are visualized in the form of parallel coordinate graphs which conclude the difference between UDP Flood attack data and normal data is clearly obtained and marked by differences in line colors in the graph produced within this study.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	JARINGAN INTERNET
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television > TK5105.5.S72 Computer networks
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Christoper Marlo
Date Deposited:	16 Jan 2023 01:49
Last Modified:	16 Jan 2023 01:49
URI:	http://repository.unsri.ac.id/id/eprint/86217

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