In this section, we are going to learn how to build different botnet detection systems with many machine learning algorithms. As a start to a first practical lab, let's start by building a machine learning-based botnet detector using different classifiers. By now, I hope you have acquired a clear understanding about the major steps of building machine learning systems. So, I believe that you already know that, as a first step, we need to look for a dataset. Many educational institutions and organizations are given a set of collected datasets from internal laboratories. One of the most well known botnet datasets is called the CTU-13 dataset. It is a labeled dataset with botnet, normal, and background traffic delivered by CTU University, Czech Republic. During their work, they tried to capture real botnet...
Mastering Machine Learning for Penetration Testing
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Mastering Machine Learning for Penetration Testing
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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)
Preface
Free Chapter
Introduction to Machine Learning in Pentesting
Phishing Domain Detection
Malware Detection with API Calls and PE Headers
Malware Detection with Deep Learning
Botnet Detection with Machine Learning
Machine Learning in Anomaly Detection Systems
Detecting Advanced Persistent Threats
Evading Intrusion Detection Systems
Bypassing Machine Learning Malware Detectors
Best Practices for Machine Learning and Feature Engineering
Assessments
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Customer Reviews