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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

Testing the adversarial robustness of neural networks

The study of adversarial attacks on neural networks has revealed a surprising sensitivity to adversarial perturbations. Even the most accurate of neural networks, when left undefended, has been shown to be vulnerable to single pixel attacks and the peppering of invisible-to-the-human-eye noise. Fortunately, recent advances in the field have offered solutions on how to harden neural networks to adversarial attacks of all sorts. One such solution is a neural network design called Analysis by Synthesis (ABS). The main idea behind the model is that it is a Bayesian model. Rather than directly predicting the label given the input, the model also learns class-conditional, sample distributions ...

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Publisher Resources

ISBN: 9781789614671Supplemental Content