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Learning Failure-Inducing Models for Testing Software-Defined Networks. (arXiv:2210.15469v1 [cs.SE])
Oct. 28, 2022, 1:11 a.m. | Raphaël Ollando, Seung Yeob Shin, Lionel C. Briand
cs.LG updates on arXiv.org arxiv.org
Software-defined networks (SDN) enable flexible and effective communication
systems, e.g., data centers, that are managed by centralized software
controllers. However, such a controller can undermine the underlying
communication network of an SDN-based system and thus must be carefully tested.
When an SDN-based system fails, in order to address such a failure, engineers
need to precisely understand the conditions under which it occurs. In this
paper, we introduce a machine learning-guided fuzzing method, named FuzzSDN,
aiming at both (1) generating effective …
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