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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

ML attacks on PUFs

Classical cryptography offers several measures for securing electronic devices. These mainly rely on a secret key and expensive resources due to the device permanently storing a piece of digital information that's unknown to our adversaries. In practice, it is difficult to keep this information confidential. This problem motivated the invention of PUF – physical devices that produce an output that's quick to evaluate yet hard to predict.

To authenticate using a PUF, we need to construct a database of Challenge-Response Pairs (CRPs). A challenge is a binary string (for example, 1100101...01) of length n, and a response is some other binary string of length m. To find out whether an unknown device is the aforementioned PUF, we need to issue it a number of challenges, verifying that it produces the correct responses until we reach the desired...

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