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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

Deep-pwning

Deep-pwning is a framework for evaluating the robustness of machine learning tools against adversarial attacks. It has become widely known in the data science community that naive machine learning models, such as deep neural networks trained with the sole aim of classifying images, are very easily fooled.

The following diagram shows Explaining and Harnessing Adversarial Examples, I. J. Goodfellow et al:

Cybersecurity being an adversarial field of battle, a machine learning model used to secure from attackers ought to be robust against adversaries. As a consequence, it is important to not only report the usual performance metrics, ...

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Publisher Resources

ISBN: 9781789614671Supplemental Content