OPTIMALISASI MODEL DETEKSI SERANGAN SMURF DDOS PADA SOFTWARE DEFINED NETWORK MENGGUNAKAN METODE DECISION TREE DAN K-NEAREST NEIGHBOR

MAIS, AL and Heryanto, Ahmad and Septian, Tri Wanda (2022) OPTIMALISASI MODEL DETEKSI SERANGAN SMURF DDOS PADA SOFTWARE DEFINED NETWORK MENGGUNAKAN METODE DECISION TREE DAN K-NEAREST NEIGHBOR. Undergraduate thesis, Sriwijaya University.

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Abstract

This study focuses on the dataset from DDoS Attack SDN Dataset (2020) where the Smurf DDoS attack focuses on the ICMP protocol. The purpose of this study is to detect Smurf DDoS attacks on the Software Defined Network topology and analyze the accuracy and performance as well as compare two machine learning methods used. The method used in this study is the Decision Tree and K-Nearest Neighbor and Pearson Correlation as a feature selection in the dataset. The results of this study show that the Decision Tree has a better accuracy and performance value of 97.05% compared to the K-Nearest Neighbor value of 93.42% by using a dataset that has gone through a feature selection process. The parameter values used in each method also greatly affect the resulting accuracy and performance values in detecting Smurf DDoS attacks on the network.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Sofware, Optimalisasi
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:	Al Mais
Date Deposited:	21 Dec 2022 07:36
Last Modified:	21 Dec 2022 07:36
URI:	http://repository.unsri.ac.id/id/eprint/84396

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