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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 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

Penetration Testing Using Machine Learning

A penetration test, aka a pen test, is an authorized simulated cyberattack on an information system, designed to elicit security vulnerabilities. In this chapter, we will be covering a wide selection of machine learning-technologies for penetration testing and security countermeasures. We'll begin by cracking a simple CAPTCHA system. We'll cover the automatic discovery of software vulnerabilities using deep learning, using fuzzing and code gadgets. We'll demonstrate enhancements to Metasploit, as well as covering how to assess the robustness of machine learning systems to adversarial attacks. Finally, we'll cover more specialized topics, such as deanonymizing Tor traffic, recognizing unauthorized access via keystroke dynamics, and detecting malicious URLs.

This chapter covers the following recipes:

  • CAPTCHA breaker...
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