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
1
Basics of Machine Learning in Cybersecurity
5
Using Data Science to Catch Email Fraud and Spam

Catching Impersonators and Hackers Red Handed

Impersonation attacks are the form of cyber attack that has evolved the most in recent years. Impersonation in its most basic form is the act of pretexting as another person. Pretexting is the basic form of social engineering, where a person mimics another person to obtain data or resources that have been assigned to the privileged person only.

To understand impersonation attacks better, and to detect the different attacks and see how machine learning can solve them, we will go through the following topics:

  • Understanding impersonation
  • Different types of impersonation fraud
  • Understanding Levenshtein distance
  • Use case on finding malicious domain similarity
  • Use case to detect authorship attribution