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Machine Learning for Cybersecurity Cookbook
book

Machine Learning for Cybersecurity Cookbook

by Emmanuel Tsukerman
November 2019
Intermediate to advanced content levelIntermediate to advanced
346 pages
9h 36m
English
Packt Publishing
Content preview from Machine Learning for Cybersecurity Cookbook

How it works…

Most popular fuzzers, while effective in some limited situations, often get stuck in a loop. Gradient-based methods, such as the one discussed here, are promising but do not clearly apply to the problem, because real-world program behaviors are not necessarily smooth functions (for example, they can be discontinuous). The idea behind NEUZZ is to approximate the program's behavior as a smooth function using neural networks. Then, it is possible to apply gradient methods to improve fuzzing efficiency. We start our recipe by compiling NEUZZ (step 1). The funroll-loops flag causes the compiler to unroll loops whose number of iterations can be determined at compile time or upon entry to the loop. As a result, the code is larger, ...

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Publisher Resources

ISBN: 9781789614671Supplemental Content