TY  - CHAP
ID  - SisLab4485
UR  - https://link.springer.com/chapter/10.1007%2F978-3-030-73280-6_31
A1  - Can, Duy Cat
A1  - Le, Hoang Quynh
A1  - Ha, Quang Thuy
Y1  - 2021/04/07/
N2  - 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 2019 dataset 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  - Springer
TI  - Detection of Distributed Denial of Service Attacks Using Automatic Feature Selection with Enhancement for Imbalance Dataset
SP  - 386
AV  - none
EP  - 398
T2  - ACIIDS 2021: Intelligent Information and Database Systems
ER  -