Klasifikasi Serangan Distributed Denial of Service (DDoS) Menggunakan Random Forest Dengan CFS
| Main Authors: | Bhaskara, I Made Wasanta, Suputra, I Putu Gede Hendra, Widiartha, I Made, Kadyanan, I Gusti Agung Gede Arya, Putra, I Gusti Ngurah Anom Cahyadi, Dwidasmara, Ida Bagus Gede |
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| Format: | Article info application/pdf eJournal |
| Bahasa: | ind |
| Terbitan: |
Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University
, 2022
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| Online Access: |
https://ojs.unud.ac.id/index.php/JLK/article/view/87013 https://ojs.unud.ac.id/index.php/JLK/article/view/87013/47244 |
Daftar Isi:
- 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.