TY  - INPR
ID  - SisLab4289
UR  - https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4289/
A1  - Can, Duy Cat
A1  - Le, Hoang Quynh
A1  - Ha, Quang Thuy
Y1  - 2021/04/07/
N2  - Abstract: With  the  development  of  technology,  the  highly  accessible internet service is the biggest demand for most people. Online network, however, has been suffering from malicious attempts to disrupt essential web technologies, resulting in service failures. In this work, we introduced a model to detect and classify Distributed Denial of Service attacks based on  neural  networks  that  take  advantage  of  a  proposed  automatic  feature selection component. The experimental results on CIC-DDoS 2019dataset have demonstrated that our proposed model outperformed other machine learning-based model by large margin. We also investigated the effectiveness of weighted loss and hinge loss on handling the class imbalance problem.
PB  - ACIIDS 2021
TI  - Detection of Distributed Denial of Service Attacks using Automatic Feature Selection with Enhancement for Imbalance Dataset
AV  - public
T2  - ACIIDS-2021
ER  -