Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/77335
Title: Machine learning aided traffic tolerance to improve resilience for software defined networks
Authors: Gangadhar, S
Sterbenz, JPG 
Issue Date: 2017
Source: Proceedings of 2017 9th International Workshop on Resilient Networks Design and Modeling, RNDM 2017, 4-6 Sept 2017, 8093035
Abstract: Software Defined Networks (SDNs) have gained prominence recently due to their flexible management and superior configuration functionality of the underlying network. SDNs, with OpenFlow as their primary implementation, allow for the use of a centralised controller to drive the decision making for all the supported devices in the network and manage traffic through routing table changes for incoming flows. In conventional networks, machine learning has been shown to detect malicious intrusion, and classify attacks such as DoS, user to root, and probe attacks. In this work, we extend the use of machine learning to improve traffic tolerance for SDNs. To achieve this, we extend the functionality of the controller to include a resilience framework, ReSDN, that incorporates machine learning to be able to distinguish DoS attacks, focussing on a neptune attack for our experiments. Our model is trained using the MIT KDD 1999 dataset. The system is developed as a module on top of the POX controller platform and evaluated using the Mininet simulator.
Keywords: DoS attack
Future Internet
Machine learning
Network security
OpenFlow
Resilience
SDN
Survivability
SYN flood
Traffic tolerance
Publisher: Institute of Electrical and Electronics Engineers Inc.
ISBN: 9781538606711
DOI: 10.1109/RNDM.2017.8093035
Appears in Collections:Conference Paper

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