Mendeley
TY  _  JOUR
ID  -  13990915251087
TI  -  Self-Organization Map (SOM) Algorithm for DDoS Attack Detection in Distributed Software Defined Network (D-SDN)
JO  -  Journal of Information Systems and Telecommunication (JIST) 
JA  -  ES
LA  -  en
SN  -  2322-1437
AU  -  Rafiee Mohsen
AU  -  shirmarz Alireza 
AD  -  Mazandaran University
AD  -  UFSCar
Y1  -  2022
PY  -  2022
VL  -  38
IS  -  
SP  -  120
EP  -  131
KW  -  Software Defined Network (SDN)
KW  -  Distributed Controller 
KW  -  Distributed denial-of-service (DDoS) 
KW  -  Self-Organizing Map (SOM)
KW  -  Learning Vector Quantization (LVQ)
DO  -  
N2  -  The extend of the internet across the world has increased cyber-attacks and threats. One of the most significant threats includes denial-of-service (DoS) which causes the server or network not to be able to serve. This attack can be done by distributed nodes in the network as if the nodes collaborated. This attack is called distributed denial-of-service (DDoS). There is offered a novel architecture for the future networks to make them more agile, programmable and flexible. This architecture is called software defined network (SDN) that the main idea is data and control network flows separation. This architecture allows the network administrator to resist DDoS attacks in the centralized controller. The main issue is to detect DDoS flows in the controller. In this paper, the Self-Organizing Map (SOM) method and Learning Vector Quantization (LVQ) are used for DDoS attack detection in SDN with distributed architecture in the control layer. To evaluate the proposed model, we use a labelled data set to prove the proposed model that has improved the DDoS attack flow detection by 99.56%. This research can be used by the researchers working on SDN-based DDoS attack detection improvement.
UR  - rimag.ir/en/Article/15644
L1  - rimag.ir/en/Article/Download/15644
ER  -  
