A New Machine Learning-based Collaborative DDoS Mitigation Mechanism in Software-Defined Network

Saif Saad Mohammed; Rasheed Hussain; Oleg Senko; Bagdat Bimaganbetov; Joo Young Lee; Fatima Hussain; Chaker Abdelaziz Kerrache; Ezedin Barka; Md Zakirul Alam Bhuiyan

doi:10.1109/WiMOB.2018.8589104

A New Machine Learning-based Collaborative DDoS Mitigation Mechanism in Software-Defined Network

Saif Saad Mohammed
, Rasheed Hussain
, Oleg Senko
, Bagdat Bimaganbetov
, Joo Young Lee
, Fatima Hussain
, Chaker Abdelaziz Kerrache
, Ezedin Barka
, Md Zakirul Alam Bhuiyan

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

52 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2018
Publisher	IEEE Computer Society
ISBN (Electronic)	9781538668764
DOIs	https://doi.org/10.1109/WiMOB.2018.8589104
Publication status	Published - 26 Dec 2018
Event	14th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2018 - Limassol, Cyprus Duration: 15 Oct 2018 → 17 Oct 2018

Publication series

Name	International Conference on Wireless and Mobile Computing, Networking and Communications
Volume	2018-October
ISSN (Print)	2161-9646
ISSN (Electronic)	2161-9654

Conference

Conference	14th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2018
Country/Territory	Cyprus
City	Limassol
Period	15/10/18 → 17/10/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

DDoS attacks
Machine Learning
SDN
Security

Access to Document

10.1109/WiMOB.2018.8589104

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Mohammed, S. S., Hussain, R., Senko, O., Bimaganbetov, B., Lee, J. Y., Hussain, F., Kerrache, C. A., Barka, E., & Alam Bhuiyan, M. Z. (2018). A New Machine Learning-based Collaborative DDoS Mitigation Mechanism in Software-Defined Network. In 2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2018 Article 8589104 (International Conference on Wireless and Mobile Computing, Networking and Communications; Vol. 2018-October). IEEE Computer Society. https://doi.org/10.1109/WiMOB.2018.8589104

Mohammed, Saif Saad ; Hussain, Rasheed ; Senko, Oleg et al. / A New Machine Learning-based Collaborative DDoS Mitigation Mechanism in Software-Defined Network. 2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2018. IEEE Computer Society, 2018. (International Conference on Wireless and Mobile Computing, Networking and Communications).

@inproceedings{f303dc8bf68147ee80122047c6c6d208,

title = "A New Machine Learning-based Collaborative DDoS Mitigation Mechanism in Software-Defined Network",

abstract = "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.",

keywords = "DDoS attacks, Machine Learning, SDN, Security",

author = "Mohammed, \{Saif Saad\} and Rasheed Hussain and Oleg Senko and Bagdat Bimaganbetov and Lee, \{Joo Young\} and Fatima Hussain and Kerrache, \{Chaker Abdelaziz\} and Ezedin Barka and \{Alam Bhuiyan\}, \{Md Zakirul\}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 14th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2018 ; Conference date: 15-10-2018 Through 17-10-2018",

year = "2018",

month = dec,

day = "26",

doi = "10.1109/WiMOB.2018.8589104",

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Mohammed, SS, Hussain, R, Senko, O, Bimaganbetov, B, Lee, JY, Hussain, F, Kerrache, CA, Barka, E & Alam Bhuiyan, MZ 2018, A New Machine Learning-based Collaborative DDoS Mitigation Mechanism in Software-Defined Network. in 2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2018., 8589104, International Conference on Wireless and Mobile Computing, Networking and Communications, vol. 2018-October, IEEE Computer Society, 14th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2018, Limassol, Cyprus, 15/10/18. https://doi.org/10.1109/WiMOB.2018.8589104

A New Machine Learning-based Collaborative DDoS Mitigation Mechanism in Software-Defined Network. / Mohammed, Saif Saad; Hussain, Rasheed; Senko, Oleg et al.
2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2018. IEEE Computer Society, 2018. 8589104 (International Conference on Wireless and Mobile Computing, Networking and Communications; Vol. 2018-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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

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

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M3 - Conference Contribution (Conference Proceeding)

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 -

Mohammed SS, Hussain R, Senko O, Bimaganbetov B, Lee JY, Hussain F et al. A New Machine Learning-based Collaborative DDoS Mitigation Mechanism in Software-Defined Network. In 2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2018. IEEE Computer Society. 2018. 8589104. (International Conference on Wireless and Mobile Computing, Networking and Communications). doi: 10.1109/WiMOB.2018.8589104

A New Machine Learning-based Collaborative DDoS Mitigation Mechanism in Software-Defined Network

Abstract

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Keywords

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