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CSDSM: Cognitive Switch-based DDoS Sensing and Mitigation in SDN-driven CDNi Word

Nishat I Mowla¹, Inshil Doh² and Kijoon Chae³

1. Department of Computer Science and Engineering, Ewha Womans University
   51, Ewhayeodaegil, Seodaemungu, Seoul, 03760, Korea
   nishat.i.mowla@gmail.com
2. Department of Cyber Security, Ewha Womans University
   51, Ewhayeodaegil, Seodaemungu, 03760, Korea
   isdoh1@ewha.ac.kr
3. Department of Computer Science and Engineering, Ewha Womans University
   51, Ewhayeodaegil, Seodaemungu, 03760, Korea
   kjchae@ewha.ac.kr

Abstract

Content Delivery Networks (CDNs) are increasingly deployed for their efficient content delivery and are often integrated with Software Defined Networks (SDNs) to achieve centrality and programmability of the network. However, these networks are also an attractive target for network attackers whose main goal is to exhaust network resources. One attack approach is to over-flood the OpenFlow switch tables containing routing information. Due to the increasing number of different flooding attacks such as DDoS, it becomes difficult to distinguish these attacks from normal traffic when evaluated with traditional attack detection methods. This paper proposes an architectural method that classifies and defends all possible forms of DDoS attack and legitimate Flash Crowd traffic using a segregated dimension functioning cognitive process based in a controller module. Our results illustrate that the proposed model yields significantly enhanced performance with minimal false positives and false negatives when classified with optimal Support Vector Machine and Logistic Regression algorithms. The traffic classifications initiate deployment of security rules to the OpenFlow switches, preventing new forms of flooding attacks. To the best of our knowledge, this is the first work conducted on SDN-driven CDNi used to detect and defend against all possible DDoS attacks through traffic segregated dimension functioning coupled with cognitive classification.

Key words

SDN, CDN, CDNi, DDoS, Flash Crowd, Machine Learning, Support Vector Machine, Logistic Regression

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS170328044M

Publication information

Volume 15, Issue 1 (January 2018)
Year of Publication: 2018
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

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How to cite

Mowla, N. I., Doh, I., Chae, K.: CSDSM: Cognitive Switch-based DDoS Sensing and Mitigation in SDN-driven CDNi Word. Computer Science and Information Systems, Vol. 15, No. 1, 163–185. (2018), https://doi.org/10.2298/CSIS170328044M