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

Decision Tree and Context-Based Malicious Event Detection

Malware destructs computer exploits that are responsible for increased CPU usage, slower computer speeds, and much more. It reduces network speeds, freezes or crashes systems, and modifies or deletes files. Malware often messes with default computer configurations and performs strange computer activities with or without the knowledge of a user.

Malicious software is used to steal data, bypass firewalls, and handicap access controls. Malware, at the end of the day, hosts some sort of malicious code and can be categorized into multiple types based on the type of malicious activity it performs.

Next, we will discuss a list of such malware and the injections that it performs:

  • Types of malware
  • Malicious data injection in databases
  • Malicious data injection in wireless networks
  • Intrusion detection with decision tree
  • Malicious...