eprintid: 4485
rev_number: 8
eprint_status: archive
userid: 307
dir: disk0/00/00/44/85
datestamp: 2021-06-18 11:17:30
lastmod: 2021-06-18 11:17:30
status_changed: 2021-06-18 11:17:30
type: book_section
metadata_visibility: show
creators_name: Can, Duy Cat
creators_name: Le, Hoang Quynh
creators_name: Ha, Quang Thuy
creators_id: catcd@vnu.edu.vn
creators_id: lhquynh@gmail.com
creators_id: thuyhq@vnu.edu.vn
corp_creators: VNU-UET
title: Detection of Distributed Denial of Service Attacks Using Automatic Feature Selection with Enhancement for Imbalance Dataset
ispublished: pub
subjects: IT
divisions: fac_fit
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 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.
date: 2021-04-07
publisher: Springer
official_url: https://link.springer.com/chapter/10.1007%2F978-3-030-73280-6_31
full_text_status: none
pagerange: 386-398
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:   Can, Duy Cat and Le, Hoang Quynh and Ha, Quang Thuy  (2021) Detection of Distributed Denial of Service Attacks Using Automatic Feature Selection with Enhancement for Imbalance Dataset.   In:  ACIIDS 2021: Intelligent Information and Database Systems.   Springer, pp. 386-398.