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Machine Learning for Cybersecurity Cookbook

You're reading from   Machine Learning for Cybersecurity Cookbook Over 80 recipes on how to implement machine learning algorithms for building security systems using Python

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Product type Paperback
Published in Nov 2019
Publisher Packt
ISBN-13 9781789614671
Length 346 pages
Edition 1st Edition
Languages
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Author (1):
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Emmanuel Tsukerman Emmanuel Tsukerman
Author Profile Icon Emmanuel Tsukerman
Emmanuel Tsukerman
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Table of Contents (11) Chapters Close

Preface 1. Machine Learning for Cybersecurity 2. Machine Learning-Based Malware Detection FREE CHAPTER 3. Advanced Malware Detection 4. Machine Learning for Social Engineering 5. Penetration Testing Using Machine Learning 6. Automatic Intrusion Detection 7. Securing and Attacking Data with Machine Learning 8. Secure and Private AI 9. Other Books You May Enjoy Appendix

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 using variational autoencoders (VAEs). More information can be found in https://arxiv.org/abs/1805.09190.

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