eprintid: 4290
rev_number: 6
eprint_status: archive
userid: 361
dir: disk0/00/00/42/90
datestamp: 2020-12-18 09:08:09
lastmod: 2020-12-18 09:08:09
status_changed: 2020-12-18 09:08:09
type: article
metadata_visibility: show
creators_name: Vuong, Thi Hong
creators_name: Nguyen, Thi Cam Van
creators_name: Ha, Quang Thuy
creators_id: hongvt_57@vnu.edu.vn
creators_id: vanntc@vnu.edu.vn
creators_id: thuyhq@vnu.edu.vn
title: N-tier machine learning-based architecture for DDoS attack detection
ispublished: inpress
subjects: IT
divisions: fac_fit
abstract: 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.
full_text_status: public
publication: Asian Conference on Intelligent Information and Database Systems
volume: 13
refereed: TRUE
citation:   Vuong, Thi Hong and Nguyen, Thi Cam Van and Ha, Quang Thuy   N-tier machine learning-based architecture for DDoS attack detection.  Asian Conference on Intelligent Information and Database Systems, 13 .       (In Press)  
document_url: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4290/1/DDos_2020.pdf