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Hands-On Machine Learning for Cybersecurity

You're reading from   Hands-On Machine Learning for Cybersecurity Safeguard your system by making your machines intelligent using the Python ecosystem

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781788992282
Length 318 pages
Edition 1st Edition
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Authors (2):
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Soma Halder Soma Halder
Author Profile Icon Soma Halder
Soma Halder
Sinan Ozdemir Sinan Ozdemir
Author Profile Icon Sinan Ozdemir
Sinan Ozdemir
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Toc

Table of Contents (13) Chapters Close

Preface 1. Basics of Machine Learning in Cybersecurity 2. Time Series Analysis and Ensemble Modeling FREE CHAPTER 3. Segregating Legitimate and Lousy URLs 4. Knocking Down CAPTCHAs 5. Using Data Science to Catch Email Fraud and Spam 6. Efficient Network Anomaly Detection Using k-means 7. Decision Tree and Context-Based Malicious Event Detection 8. Catching Impersonators and Hackers Red Handed 9. Changing the Game with TensorFlow 10. Financial Fraud and How Deep Learning Can Mitigate It 11. Case Studies 12. Other Books You May Enjoy

Summary

In this chapter, we were introduced to the detection of the different types of abnormalities in URLs, including URL blacklisting. We also learned about how to use heuristics to detect malicious pages, including using the data for the analysis and extraction of different features. This chapter also taught us how machine learning and logistic regression is used to detect malicious URLs.

We also learned about using multiclass classification, along with SVM, to detect malicious URLs. In the next chapter, we will learn about the different types of CAPTCHAs.

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