eprintid: 4289
rev_number: 17
eprint_status: archive
userid: 370
dir: disk0/00/00/42/89
datestamp: 2020-12-18 09:07:52
lastmod: 2020-12-21 09:18:16
status_changed: 2020-12-21 09:18:16
type: conference_item
succeeds: 4266
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@vnu.edu.vn
creators_id: thuyhq@vnu.edu.vn
title: Detection of Distributed Denial of Service Attacks using Automatic Feature Selection with Enhancement for Imbalance Dataset
ispublished: inpress
subjects: IT
divisions: fac_fit
abstract: 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 2019dataset 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
date_type: published
publisher: ACIIDS 2021
full_text_status: public
monograph_type: technical_report
pres_type: paper
publication: ACIIDS 2021
event_title: ACIIDS-2021
event_type: conference
refereed: TRUE
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.    (In Press)  
document_url: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4289/1/ACIIDS2021__Detection_of_Distributed_Denial_of_Service_Attacks_using_Automatic_Feature_Selection_with_Enhancement_for_Imbalance_Dataset.pdf