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

Using artificial intelligence to crack CAPTCHA

Recently, one of the popular ways of benchmarking artificially intelligent systems is its capability to detect CAPTCHA images. The notion lies that if an AI system can crack a CAPTCHA, then it can be used to solve other complicated AI problems. An artificially intelligent system cracks CAPTCHA by either image recognition or by text/character recognition. The following screenshot shows a CAPTCHA image along with a deciphered image:

Types of CAPTCHA

The different types of CAPTCHA available are as follows:

  • Reading-based CAPTCHA: These are visual preceptors. They include text recognizers and image detectors. These are difficult to crack, but the downside is that they cannot be accessed...