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Hands-On Machine Learning for Cybersecurity

You're reading from  Hands-On Machine Learning for Cybersecurity

Product type Book
Published in Dec 2018
Publisher Packt
ISBN-13 9781788992282
Pages 318 pages
Edition 1st Edition
Languages
Authors (2):
Soma Halder Soma Halder
Profile icon Soma Halder
Sinan Ozdemir Sinan Ozdemir
Profile icon Sinan Ozdemir
View More author details
Toc

Table of Contents (13) Chapters close

Preface 1. Basics of Machine Learning in Cybersecurity 2. Time Series Analysis and Ensemble Modeling 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

Characteristics of CAPTCHA

Cracking CAPTCHA is difficult and the algorithm driving it is patented. However, it was made public because CAPTCHAs are just not a novel algorithm but a difficult case of artificial intelligence. Hence, reverse engineering it is challenging.

Deciphering CAPTCHAs require three primary capabilities. When the following capabilities are used in sync, it is then that deciphering a CAPTCHA becomes difficult. The three capabilities are as follows:

  • Capacity of consistent image recognition: No matter what shape or size an alphabet appears, the human brain can automatically identify the characters.
  • Capacity of image segmentation: This is the capability to segregate one character from the other.
  • Capacity to parse images: Context is important for identifying a CAPTCHA, because often it is required to parse the entire word and derive context from the word.
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