eprintid: 4266
rev_number: 8
eprint_status: archive
userid: 370
dir: disk0/00/00/42/66
datestamp: 2020-12-14 07:27:45
lastmod: 2020-12-14 07:36:50
status_changed: 2020-12-14 07:27:45
type: monograph
metadata_visibility: no_search
creators_name: Can, Duy Cat
creators_id: catcd@vnu.edu.vn
title: Detection of Distributed Denial of Service Attacks using Automatic Feature Selection with Enhancement for Imbalance Dataset
ispublished: submitted
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: 2020
date_type: published
publisher: ACIIDS 2021
full_text_status: restricted
monograph_type: technical_report
citation:   Can, Duy Cat  (2020) Detection of Distributed Denial of Service Attacks using Automatic Feature Selection with Enhancement for Imbalance Dataset.  Technical Report. ACIIDS 2021.    (Submitted)  
document_url: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4266/1/technical_report_ACIIDS_2021.pdf