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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

Automatic Intrusion Detection

An intrusion detection system monitors a network or a collection of systems for malicious activity or policy violations. Any malicious activity or violation caught is stopped or reported. In this chapter, we will design and implement several intrusion detection systems using machine learning. We will begin with the classical problem of detecting spam email. We will then move on to classifying malicious URLs. We will take a brief detour to explain how to capture network traffic, so that we may tackle more challenging network problems, such as botnet and DDoS detection. We will construct a classifier for insider threats. Finally, we will address the example-dependent, cost-sensitive, radically imbalanced, and challenging problem of credit card fraud.

This chapter contains the following recipes:

  • Spam filtering using machine learning
  • Phishing URL detection...
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