Skripsi

DETEKSI SERANGAN DISTRIBUTED DENIAL OF SERVICE (DDoS) PADA APACHE SPARK MENGGUNAKAN METODE K-MEANS CLUSTERING.

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Penilaian

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Distributed Denial-of-Service (DDoS) is a collection of denial-of-service attacks that are carried out by executing commands from the master computer to a number of botnets which are infected hosts to attack certain targets. Because of this, DDoS attack detection is the first and most important way to counter DDoS attacks. The basis for carrying out the detection approach is using machine learning. K-means clustering is the simplest and most well-known clustering analysis algorithm in solving clustering problems. This algorithm is known to be efficient for large datasets. This paper proposes detecting DDoS attacks using an unsupervised learning method, namely K-means clustering on Apache Spark. This study used the CIC-DDoS2019 dataset from the University of New Brunswick (UNB) to train and perform experiments on the detection system used. This model produces the best evaluation results with the recall of 99,99%, precision of 99,99%, specificity of 88.24%, the accuracy of 99.98%, and F1 score of 99.99%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307001878	T102407	T1024072023	Central Library (Referens)	Available

Detail Information

Series Title

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Call Number

T1024072023

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2023

Collation

xv, 67 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

003.307

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Model dan simulasi komputer
Prodi Sistem Komputer

Specific Detail Info

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Statement of Responsibility

MURZ

Other version/related

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