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CSDSM: Cognitive switch-based DDoS sensing and mitigation in sdn-driven CDNi word

Title
CSDSM: Cognitive switch-based DDoS sensing and mitigation in sdn-driven CDNi word
Authors
Mowla N.I.Doh I.Chae K.
Ewha Authors
채기준도인실
SCOPUS Author ID
채기준scopus; 도인실scopus
Issue Date
2018
Journal Title
Computer Science and Information Systems
ISSN
1820-0214JCR Link
Citation
Computer Science and Information Systems vol. 15, no. 1, pp. 163 - 185
Keywords
CDNCDNiDDoSFlash CrowdLogistic RegressionMachine LearningSDNSupport Vector Machine
Publisher
ComSIS Consortium
Indexed
SCIE; SCOPUS WOS scopus
Document Type
Article
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. © 2018, ComSIS Consortium. All rights reserved.
DOI
10.2298/CSIS170328044M
Appears in Collections:
엘텍공과대학 > 컴퓨터공학과 > Journal papers
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