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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 FREE CHAPTER 2. Machine Learning-Based Malware Detection 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

Detecting obfuscated JavaScript

In this section, we will see how to use machine learning to detect when a JavaScript file is obfuscated. Doing so can serve to create a binary feature, obfuscated or not, to be used in benign/malicious classification, and can serve also as a prerequisite step to deobfuscating the scripts.

Getting ready

Preparation for this recipe involves installing the scikit-learn package in pip. The command is as follows:

pip install sklearn

In addition, obfuscated and non-obfuscated JavaScript files have been provided for you in the repository. Extract JavascriptSamplesNotObfuscated.7z to a folder named JavaScript Samples. Extract JavascriptSamplesObfuscated.7z to a folder named JavaScript Samples...

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