Klasifikasi Serangan Distributed Denial of Service (DDoS) Menggunakan Support Vector Machine dengan Correlation-Based Feature Selection - Varuna, I Gusti Ngurah Made Dika; Kadyanan, I Gusti Agung Gede Arya; Karyawati, Anak Agung Istri Ngurah Eka; Raharja, Made Agung (JELIKU (Jurnal Elektronik Ilmu Komputer Udayana); Vol 13 No 3 (2025): JELIKU Volume 13 No 3, February 2025DO - 10.24843/JLK.2025.v13.i03.p03, 2024)
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.