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

Time Series Analysis and Ensemble Modeling

In this chapter, we will study two important concepts of machine learning: time series analysis and ensemble learning. These are important concepts in the field of machine learning.

We use these concepts to detect anomalies within a system. We analyze historic data and compare it with the current data to detect deviations from normal activities.

The topics that will be covered in this chapter are the following:

  • Time series and its different classes
  • Time series decomposition
  • Analysis of time series in cybersecurity
  • Prediction of DDoS attack
  • Ensemble learning methods and voting ensemble methods to detect cyber attacks