SELEKSI FITUR UNTUK MENEMUKAN POLA FITUR TERBAIK PADA SISTEM PENDETEKSI SERANGAN DDOS DENGAN MENGGUNAKAN METODE K-NEAREST NEIGHBOR (K-NN)

PRATAMA, RICKY AKBAR and Heryanto, Ahmad and Septian, Tri Wanda (2023) SELEKSI FITUR UNTUK MENEMUKAN POLA FITUR TERBAIK PADA SISTEM PENDETEKSI SERANGAN DDOS DENGAN MENGGUNAKAN METODE K-NEAREST NEIGHBOR (K-NN). Undergraduate thesis, Sriwijaya University.

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Abstract

DDoS attacks are one of the main threats to security issues on the internet today which have quite a severe impact. As for knowing the best DDoS attack detection, this study applies several selection features to find the best feature pattern in detecting DDoS attacks using the K-Nearest Neighbor method. In the application of selection using several selection features, namely Random Forest Classifier (RFC), Mutual Information Classifier (MIC), Correlation Based Selection (CBS), and Lasso Regularization Regression (LRR). Based on the results of the classification using the K-Nearest Neighbor method, the mutual information classifier and random forest classifier that get the highest accuracy and are also the best at reducing features and finding the most relevant feature variables for detecting DDoS attacks.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	SISTEM PENDETEKSI SERANGAN
Subjects:	T Technology > T Technology (General) > T57.6-57.97 Operations research. Systems analysis > T57.85 Network systems theory Including network analysis Cf. TS157.5+ Scheduling
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Ricky Akbar Pratama
Date Deposited:	15 Mar 2023 08:26
Last Modified:	15 Mar 2023 08:26
URI:	http://repository.unsri.ac.id/id/eprint/90914

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