Authors:

I Made Wasanta Bhaskara, I Putu Gede Hendra Suputra, I Made Widiartha, I Gusti Agung Gede Arya Kadyanan, I Gusti Ngurah Anom Cahyadi Putra, Ida Bagus Gede Dwidasmara

Abstract:

“Distributed Denial of Service (DDoS) attacks can have serious impacts on your organization and can cause enormous losses. This attack works by sending a computer or server an amount of requests that exceeds the capabilities of that computer. When classifying DDoS attacks in this study, feature selection is performed using correlation-based feature selection (CFS). The dataset used by the author in this study is CSE-CIC-IDS 2018. Feature selection on a dataset using CFS gets the results in the form of features related to the dataset. That is, a total of 31 features with a relationship score greater than 0.1. The average precision generated by the system using the random forest method and CFS function selection is 99.784%. Accuracy is the result of using the number of trees parameter with a value of 10. For a random forest model with no feature selection, the highest accuracy is 49.501%. This indicates that changing the random forest model parameters and selecting the CFS feature will affect high accuracy.”

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PDF:

https://jurnal.harianregional.com/jlk/full-87013

Published

2022-07-08

How To Cite

BHASKARA, I Made Wasanta et al. Klasifikasi Serangan Distributed Denial of Service (DDoS) Menggunakan Random Forest Dengan CFS.JELIKU (Jurnal Elektronik Ilmu Komputer Udayana), [S.l.], v. 11, n. 2, p. 215-222, july 2022. ISSN 2654-5101. Available at: https://ojs.unud.ac.id/index.php/JLK/article/view/87013. Date accessed: 28 Aug. 2025. doi:https://doi.org/10.24843/JLK.2022.v11.i02.p01.

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Issue

Vol 11 No 2 (2022): JELIKU Volume 11 No 2, November 2022

Section

Articles

Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License