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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
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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

Knocking Down CAPTCHAs

CAPTCHA is short for Completely Automated Public Turing test to tell Computers and Humans Apart. These are tests that verify whether a computing system is being operated by a human or a robot.

CAPTCHAs were built in such a way that they would need human mediation to be administered to computing systems as a part of the authentication system to ensure system security and hence prevention of unwanted looses for organizations.

Apart from summarizing how CAPTCHA works, this chapter also covers the following topics:

  • Characteristics of CAPTCHAs
  • Using artificial intelligence to crack CAPTCHAs
  • Types of CAPTCHA
  • Solving CAPTCHAs with neural networks

The following screenshot shows a CAPTCHA image that is used for verification:

lock icon The rest of the chapter is locked
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