An autonomic testing framework for IPv6 configuration protocols

Authors:
Sheila Becker;Humberto Abdelnur;Radu State;Thomas Engel
Affiliations:
University of Luxembourg, Luxembourg;MADYNES, INRIA Nancy-Grand Est, France;University of Luxembourg, Luxembourg;University of Luxembourg, Luxembourg
Venue:
AIMS'10 Proceedings of the Mechanisms for autonomous management of networks and services, and 4th international conference on Autonomous infrastructure, management and security
Year:
2010

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Abstract

The current underutilization of IPv6 enabled services makes accesses to them very attractive because of higher availability and better response time, like the IPv6 specific services from Google and Youtube have recently got a lot of requests. In this paper, we describe a fuzzing framework for IPv6 protocols. Fuzzing is a process by which faults are injected in order to find vulnerabilities in implementations. Our paper describes a machine learning approach, that leverages reinforcement based fuzzing method. We describe a reinforcement learning algorithm to allow the framework to autonomically learn the best fuzzing mechanisms and to automatically test stability and reliability of IPv6.