@thesis{thesis,
		author={Atsari  Muhamad Rizqi Zul},
		title ={Klasifikasi Serangan DDoS Menggunakan Metode Hybrid Penggabungan Support Vector Machine (SVM) dengan Gaussian Naive Bayes},
		year={2023},
		url={https://eprints.umm.ac.id/id/eprint/1079/},
		abstract={With the increasing dependence of society on computer systems, internet security has become crucial. One of the major threats that often occurs is DDoS (Distributed Denial of Service) attacks. Previous research has shown that these attacks can disrupt internet infrastructure by flooding the target server or network with fake internet traffic. In an effort to enhance cyber attack detection, especially DDoS attacks, this research aims to develop a classification model using the Hybrid method, which combines Support Vector Machine (SVM) with Gaussian Naïve Bayes. The dataset used in this research is obtained from Kaggle. The purpose of using the hybrid method is to improve accuracy in detecting and predicting DDoS attacks by leveraging the strengths of multiple methods. It is hoped that this research can play a positive role in enhancing the classification ability to detect DDoS attacks. By implementing the Hybrid method, this research has successfully achieved an accuracy rate of 100%.}
		}