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

Case Studies

In this day and age, password security is sometimes our first line of defence against malicious activity. SplashData recently released the worst passwords of 2018 by analyzing over 5,000,000 leaked passwords and looking at the most-used passwords. The top-10 list looks like this:

  • 123456
  • password
  • 123456789
  • 12345678
  • 12345
  • 111111
  • 1234567
  • sunshine
  • qwerty
  • iloveyou

SplashData had released this list annually in an effort to encourage people to use more secure passwords.

If you or someone you know uses a password on this list for any purpose, change it immediately!

In this chapter, we will follow in the footsteps of SplashData and perform our own password analysis on over 1,000,000 passwords that were leaked for one reason or another. We will study the following topics: