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

Data parsing

We need to transform data in a format that is easily and readily readable by the feature generator. The columns that we generate comprise the following:

  • startTimeISO
  • Type of Windows event
  • Destination name or IP
  • Destination SecurityID
  • Destination username
  • Source log on type
  • Source name or IP
  • Destination NtDomain
  • Destination service security ID
  • Destination service name
  • Source username
  • Privileges
  • Source host name
  • Destination port
  • AD profile path
  • AD script path
  • AD user workstation
  • Source log on ID
  • Source security ID
  • Source NtDomain