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http://hdl.handle.net/10609/121786
Title: Machine Learning for Fuzzing: State of Art
Author: Barranca Fenollar, Pablo
Director: Enric Hernández Jiménez
Keywords: fuzzing
fuzzer
stages
coverage
mutation
filter
operators
model
learning
machine
AFL
Issue Date: 1-Jun-2020
Publisher: Universitat Oberta de Catalunya (UOC)
Published in: Pablo Barranca Fenollar;
Abstract: Machine learning has become more and more popular in recent years. This popularization has been stimulated by multiple factors: large and affordable computational power, new powerful algorithms, new tools that make it easy to use machine learning algorithms, availability of big data to train the models, etc. Many disciplines have experienced significant changes thanks to its adoption. The fuzzing field has not been an exception. Many researchers have proposed applying machine learning algorithms to the various stages of the fuzzing process. Most studies seem to have brought improvements to the task, however it is not always clear at what cost. Moreover, the reasons behind the selection of one algorithm instead of another are not clear in much of the published literature. This master thesis not only presents the benefits and disadvantages of using various machine learning algorithms in each fuzzing stage, but also identifies new promising paths that researchers should take.
Language: English
URI: http://hdl.handle.net/10609/121786
Appears in Collections:Bachelor thesis, research projects, etc.

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