TY  - CONF
ID  - SisLab4484
UR  - https://link.springer.com/chapter/10.1007%2F978-3-030-73280-6_30
A1  - V??ng, Th? H?ng
A1  - Nguyen, Thi Cam Van
A1  - Ha, Quang Thuy
Y1  - 2021/04/07/
N2  - Distributed Denial of Service (DDoS) attack is a menace to network security that aims at exhausting the target networks with malicious traffic. With simple but powerful attack mechanisms, it introduces an immense threat to the current Internet community. In this paper, we propose a novel multi-tier architecture intrusion detection model based on a machine learning method that possibly detects DDoS attacks. We evaluate our model using the newly released dataset CICDDoS2019, which contains a comprehensive variety of DDoS attacks and address the gaps of the existing current datasets. Experimental results indicated that the proposed method is more efficient than other existing ones. The experiments demonstrated that the proposed model accurately recognize DDoS attacks outperforming the state-of-the-art by F1-score.
PB  - Springer
TI  - N-Tier Machine Learning-Based Architecture for DDoS Attack Detection
SP  - 375
AV  - none
EP  - 385
ER  -