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

Ensemble learning methods

Ensemble learning methods are used to improve performance by taking the cumulative results from multiple models to make a prediction. Ensemble models overcome the problem of overfitting by considering outputs of multiple models. This helps in overlooking modeling errors from any one model.

Ensemble learning can be a problem for time series models because every data point has a time dependency. However, if we choose to look at the data as a whole, we can overlook time dependency components. Time dependency components are conventional ensemble methods like bagging, boosting, random forests, and so on.

Types of ensembling

Ensembling of models to derive the best model performance can happen in many ways...