eprintid: 4484
rev_number: 9
eprint_status: archive
userid: 307
dir: disk0/00/00/44/84
datestamp: 2021-06-18 11:14:51
lastmod: 2021-06-18 11:14:51
status_changed: 2021-06-18 11:14:51
type: conference_item
metadata_visibility: show
creators_name: Vương, Thị Hồng
creators_name: Nguyen, Thi Cam Van
creators_name: Ha, Quang Thuy
creators_id: thuyhq@vnu.edu.vn
corp_creators: VNU-UET
title: N-Tier Machine Learning-Based Architecture for DDoS Attack Detection
ispublished: pub
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.
date: 2021-04-07
date_type: published
publisher: Springer
official_url: https://link.springer.com/chapter/10.1007%2F978-3-030-73280-6_30
full_text_status: none
pagerange: 375-385
refereed: TRUE
book_title: ACIIDS 2021: Intelligent Information and Database Systems
related_url_url: https://link.springer.com/chapter/10.1007%2F978-3-030-73280-6
related_url_type: pub
funders: VNU-UET
projects: KC.01.28/16-20
citation:   Vương, Thị Hồng and Nguyen, Thi Cam Van and Ha, Quang Thuy  (2021) N-Tier Machine Learning-Based Architecture for DDoS Attack Detection.  In: UNSPECIFIED.