TY - GEN
T1 - A New Machine Learning-based Collaborative DDoS Mitigation Mechanism in Software-Defined Network
AU - Mohammed, Saif Saad
AU - Hussain, Rasheed
AU - Senko, Oleg
AU - Bimaganbetov, Bagdat
AU - Lee, Joo Young
AU - Hussain, Fatima
AU - Kerrache, Chaker Abdelaziz
AU - Barka, Ezedin
AU - Alam Bhuiyan, Md Zakirul
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/26
Y1 - 2018/12/26
N2 - Software Defined Network (SDN) is a revolutionary idea to realize software-driven network with the separation of control and data planes. In essence, SDN addresses the problems faced by the traditional network architecture; however, it may as well expose the network to new attacks. Among other attacks, distributed denial of service (DDoS) attacks are hard to contain in such software-based networks. Existing DDoS mitigation techniques either lack in performance or jeopardize the accuracy of the attack detection. To fill the voids, we propose in this paper a machine learning-based DDoS mitigation technique for SDN. First, we create a model for DDoS detection in SDN using NSL-KDD dataset and then after training the model on this dataset, we use real DDoS attacks to assess our proposed model. Obtained results show that the proposed technique equates favorably to the current techniques with increased performance and accuracy.
AB - Software Defined Network (SDN) is a revolutionary idea to realize software-driven network with the separation of control and data planes. In essence, SDN addresses the problems faced by the traditional network architecture; however, it may as well expose the network to new attacks. Among other attacks, distributed denial of service (DDoS) attacks are hard to contain in such software-based networks. Existing DDoS mitigation techniques either lack in performance or jeopardize the accuracy of the attack detection. To fill the voids, we propose in this paper a machine learning-based DDoS mitigation technique for SDN. First, we create a model for DDoS detection in SDN using NSL-KDD dataset and then after training the model on this dataset, we use real DDoS attacks to assess our proposed model. Obtained results show that the proposed technique equates favorably to the current techniques with increased performance and accuracy.
KW - DDoS attacks
KW - Machine Learning
KW - SDN
KW - Security
UR - http://www.scopus.com/inward/record.url?scp=85060791854&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060791854&partnerID=8YFLogxK
U2 - 10.1109/WiMOB.2018.8589104
DO - 10.1109/WiMOB.2018.8589104
M3 - Conference contribution
AN - SCOPUS:85060791854
T3 - International Conference on Wireless and Mobile Computing, Networking and Communications
BT - 2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2018
PB - IEEE Computer Society
T2 - 14th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2018
Y2 - 15 October 2018 through 17 October 2018
ER -