IEEE.DDoS.Attack.Detection.Scheme.Based.On.Entropy.And.PSO-BP.Neural.Network.In.SDN.2019.RETAiL.eBook-iDiB
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| IEEE.DDoS.Attack.Detection.Scheme.Based.On.Entropy.And.PSO-BP. |
| .Neural.Network.In.SDN.2019.RETAiL.eBook-iDiB |
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. .
PACKS________: idib-dadsnn
RELEASE DATE_: 2019.07.28
YEAR_________: 2019
LANGUAGE_____: English
RELEASE SIZE_: 1 Mb - 01 files
PAGES________: 12
SOURCE_______: https://ieeexplore.ieee.org/document/8766915
PROTECTION___: DRM(removed)
FORMAT_______: .pdf
ISBN CODE____: 1673-5447
GENRE________: Scientific papers
NOTES________: Enjoy
PLOT_________: Abstract: SDN (Software Defined Network) has
many security problems, and DDoS attack is
undoubtedly the most serious harm to SDN
architecture network. How to accurately and
effectively detect DDoS attacks has always
been a difficult point and focus of SDN
security research. Based on the
characteristics of SDN, a DDoS attack
detection method combining generalized
entropy and PSO-BP neural network is
proposed. The traffic is pre-detected by the
generalized entropy method deployed on the
switch, and the detection result is divided
into normal and abnormal. Locate the switch
that issued the abnormal alarm. The
controller uses the PSO-BP neural network to
detect whether a DDoS attack occurs by
further extracting the flow features of the
abnormal switch. Experiments show that
compared with other methods, the detection
accurate rate is guaranteed while the CPU
load of the controller is reduced, and the
detection capability is better.
QUOTE________: ' '
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Files
| Path | Size |
| dadsnna.zip | 1,06 MB |
| idib.nfo | 4,88 KB |