KLASIFIKASI SERANGAN DDOS PADA JARINGAN IOT DENGAN MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBORS (KNN)

RIZIEQ, SAID USMAN and Stiawan, Deris (2024) KLASIFIKASI SERANGAN DDOS PADA JARINGAN IOT DENGAN MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBORS (KNN). Undergraduate thesis, Sriwijaya University.

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Abstract

In recent years, Internet of Things (IoT) sensors have become increasingly integrated into various devices and fields. Therefore, the security of IoT sensors is becoming more vulnerable to attacks. In this research, the K-Nearest Neighbors (KNN) algorithm is employed to classify attacks in the dataset. The dataset is obtained from interactions between DDos attacks and normal interactions, and it is balanced using the ADASYN oversampling technique. The best model achieves an accuracy of 89.29%. Comparing the parameters in KNN, the model consistently shows results with an average accuracy of 85.94%. These results indicate that the model exhibits consistent and reliable performance in the given classification task.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Internet Of Things, Klasifikasi Serangan
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Said Usman Rizieq
Date Deposited:	17 Jan 2024 06:26
Last Modified:	17 Jan 2024 06:26
URI:	http://repository.unsri.ac.id/id/eprint/138543

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