Book Image

Mastering Machine Learning for Penetration Testing

By : Chiheb Chebbi
Book Image

Mastering Machine Learning for Penetration Testing

By: Chiheb Chebbi

Overview of this book

Cyber security is crucial for both businesses and individuals. As systems are getting smarter, we now see machine learning interrupting computer security. With the adoption of machine learning in upcoming security products, it’s important for pentesters and security researchers to understand how these systems work, and to breach them for testing purposes. This book begins with the basics of machine learning and the algorithms used to build robust systems. Once you’ve gained a fair understanding of how security products leverage machine learning, you'll dive into the core concepts of breaching such systems. Through practical use cases, you’ll see how to find loopholes and surpass a self-learning security system. As you make your way through the chapters, you’ll focus on topics such as network intrusion detection and AV and IDS evasion. We’ll also cover the best practices when identifying ambiguities, and extensive techniques to breach an intelligent system. By the end of this book, you will be well-versed with identifying loopholes in a self-learning security system and will be able to efficiently breach a machine learning system.
Table of Contents (13 chapters)

Introduction to Machine Learning in Pentesting

Currently, machine learning techniques are some of the hottest trends in information technology. They impact every aspect of our lives, and they affect every industry and field. Machine learning is a cyber weapon for information security professionals. In this book, readers will not only explore the fundamentals behind machine learning techniques, but will also learn the secrets to building a fully functional machine learning security system. We will not stop at building defensive layers; we will illustrate how to build offensive tools to attack and bypass security defenses. By the end of this book, you will be able to bypass machine learning security systems and use the models constructed in penetration testing (pentesting) missions.

In this chapter, we will cover:

  • Machine learning models and algorithms
  • Performance evaluation metrics
  • Dimensionality reduction
  • Ensemble learning
  • Machine learning development environments and Python libraries
  • Machine learning in penetration testing – promises and challenges