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

Basics of Machine Learning in Cybersecurity

The goal of this chapter is to introduce cybersecurity professionals to the basics of machine learning. We introduce the overall architecture for running machine learning modules and go through in great detail the different subtopics in the machine learning landscape.

There are many books on machine learning that deal with practical use cases, but very few address the cybersecurity and the different stages of the threat life cycle. This book is aimed at cybersecurity professionals who are looking to detect threats by applying machine learning and predictive analytics.

In this chapter, we go through the basics of machine learning. The primary areas that we cover are as follows:

  • Definitions of machine learning and use cases
  • Delving into machine learning in the cybersecurity world
  • Different types of machine learning systems
  • Different data preparation techniques
  • Machine learning architecture
  • A more detailed look at statistical models and machine learning models
  • Model tuning to ensure model performance and accuracy
  • Machine learning tools