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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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Toc

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

Extracting N-grams quickly using the hash-gram algorithm

In this section, we demonstrate a technique for extracting the most frequent N-grams quickly and memory-efficiently. This allows us to make the challenges that come with the immense number of N-grams easier. The technique is called Hash-Grams, and relies on hashing the N-grams as they are extracted. A property of N-grams is that they follow a power law that ensures that hash collisions have an insignificant impact on the quality of the features thus obtained.

Getting ready

Preparation for this recipe involves installing nltk in pip. The command is as follows:

pip install nltk

In addition, benign and malicious files have been provided for you in the PE Samples Dataset...

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