Klasifikasi Serangan Distributed Denial of Service (DDoS) Menggunakan Random Forest Dengan CFS - 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 (JELIKU (Jurnal Elektronik Ilmu Komputer Udayana); Vol 11 No 2 (2022): JELIKU Volume 11 No 2, November 2022DO - 10.24843/JLK.2022.v11.i02.p01, 2022)
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.