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

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Penilaian

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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.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307000130	T84396	T843962022	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T843962022

Publisher

Inderalaya : Jurusan Sistem Komputer, Fakultas Ilmu Komputer., 2022

Collation

xii, 67 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

006.307

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Sistem Pakar
Jurusan Sistem Komputer

Specific Detail Info

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Statement of Responsibility

SEPTA

Other version/related

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