Book Image

Hands-On Machine Learning for Cybersecurity

By : Soma Halder, Sinan Ozdemir
Book Image

Hands-On Machine Learning for Cybersecurity

By: Soma Halder, Sinan Ozdemir

Overview of this book

Cyber threats today are one of the costliest losses that an organization can face. In this book, we use the most efficient tool to solve the big problems that exist in the cybersecurity domain. The book begins by giving you the basics of ML in cybersecurity using Python and its libraries. You will explore various ML domains (such as time series analysis and ensemble modeling) to get your foundations right. You will implement various examples such as building system to identify malicious URLs, and building a program to detect fraudulent emails and spam. Later, you will learn how to make effective use of K-means algorithm to develop a solution to detect and alert you to any malicious activity in the network. Also learn how to implement biometrics and fingerprint to validate whether the user is a legitimate user or not. Finally, you will see how we change the game with TensorFlow and learn how deep learning is effective for creating models and training systems
Table of Contents (13 chapters)
Free Chapter
Basics of Machine Learning in Cybersecurity
Using Data Science to Catch Email Fraud and Spam

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.