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Hands-On Machine Learning for Cybersecurity

Hands-On Machine Learning for Cybersecurity

By : Halder, Sinan Ozdemir
2.7 (6)
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Hands-On Machine Learning for Cybersecurity

Hands-On Machine Learning for Cybersecurity

2.7 (6)
By: 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)
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1
Basics of Machine Learning in Cybersecurity
5
Using Data Science to Catch Email Fraud and Spam

Preface

The damage that cyber threats can wreak upon an organization can be incredibly costly. In this book, we use the most efficient and effective tools to solve the big problems that exist in the cybersecurity domain and provide cybersecurity professionals with the knowledge they need to use machine learning algorithms. This book aims to bridge the gap between cybersecurity and machine learning, focusing on building new and more effective solutions to replace traditional cybersecurity mechanisms and provide a collection of algorithms that empower systems with automation capabilities.

This book walks you through the major phases of the threat life cycle, detailing how you can implement smart solutions for your existing cybersecurity products and effectively build intelligent and future-proof solutions. We'll look at the theory in depth, but we'll also study practical applications of that theory, framed in the contexts of real-world security scenarios. Each chapter is focused on self-contained examples for solving real-world concerns using machine learning algorithms such as clustering, k-means, linear regression, and Naive Bayes.

We begin by looking at the basics of machine learning in cybersecurity using Python and its extensive library support. You will explore various machine learning domains, including time series analysis and ensemble modeling, to get your foundations right. You will build a system to identify malicious URLs, and build a program for detecting fraudulent emails and spam. After that, you will learn how to make effective use of the k-means algorithm to develop a solution to detect and alert you about any malicious activity in the network. Also, you'll learn how to implement digital biometrics and fingerprint authentication to validate whether the user is a legitimate user or not.

This book takes a solution-oriented approach to helping you solve existing cybersecurity issues.

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