IEEE.DDoS.Attack.Detection.Scheme.Based.On.Entropy.And.PSO-BP.Neural.Network.In.SDN.2019.RETAiL.eBook-iDiB

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0day

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iDiB

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Date

2019-07-28

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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.               
   
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