Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/106274
Full metadata record
DC FieldValueLanguage
dc.contributor.authorGao, Lijunen
dc.contributor.authorLi, Yantingen
dc.contributor.authorZhang, Luen
dc.contributor.authorLin, Fengen
dc.contributor.authorMa, Maodeen
dc.date.accessioned2019-06-20T09:19:32Zen
dc.date.accessioned2019-12-06T22:07:50Z-
dc.date.available2019-06-20T09:19:32Zen
dc.date.available2019-12-06T22:07:50Z-
dc.date.issued2019en
dc.identifier.citationGao, L., Li, Y., Zhang, L., Lin, F., & Ma, M. (2019). Research on detection and defense mechanisms of DoS attacks based on BP neural network and game theory. IEEE Access, 7, 43018-43030. doi:10.1109/ACCESS.2019.2905812en
dc.identifier.urihttps://hdl.handle.net/10356/106274-
dc.description.abstractDoS (Denial of Service) attacks are becoming one of the most serious security threats to global networks. We analyze the existing DoS detection methods and defense mechanisms in depth. In this paper, BP (back propagation) neural networks and game theory are introduced to design detection methods and defense mechanisms for the DoS attacks. The BP neural network DoS attacks detection model uses KDDCUP99 as the dataset and selects multiple feature vectors from the dataset that can efficiently identify DoS attacks by large-scale training, which improves the accuracy of detecting DoS attacks to 99.977%. Furthermore, we use game theory to perform secondary analysis on DoS attacks that are not recognized by the neural network model, so that the detection rate of Dos attacks increases from 99.97% to 99.998%. Finally, we propose a DoS attacks defense strategy based on game theory. The simulation results show that the proposed detection method and defense strategy are effective for DoS attacks.en
dc.format.extent13 p.en
dc.language.isoenen
dc.relation.ispartofseriesIEEE Accessen
dc.rights© 2019 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.en
dc.subjectDoS Attacksen
dc.subjectSecurityen
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen
dc.titleResearch on detection and defense mechanisms of DoS attacks based on BP neural network and game theoryen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.identifier.doi10.1109/ACCESS.2019.2905812en
dc.description.versionPublished versionen
item.grantfulltextopen-
item.fulltextWith Fulltext-
Appears in Collections:EEE Journal Articles

SCOPUSTM   
Citations 20

8
Updated on Mar 4, 2021

PublonsTM
Citations 20

4
Updated on Mar 9, 2021

Page view(s)

207
Updated on May 21, 2022

Download(s) 50

136
Updated on May 21, 2022

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.