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Mastering Machine Learning for Penetration Testing

You're reading from   Mastering Machine Learning for Penetration Testing Develop an extensive skill set to break self-learning systems using Python

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788997409
Length 276 pages
Edition 1st Edition
Languages
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Author (1):
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Chiheb Chebbi Chiheb Chebbi
Author Profile Icon Chiheb Chebbi
Chiheb Chebbi
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Table of Contents (13) Chapters Close

Preface 1. Introduction to Machine Learning in Pentesting FREE CHAPTER 2. Phishing Domain Detection 3. Malware Detection with API Calls and PE Headers 4. Malware Detection with Deep Learning 5. Botnet Detection with Machine Learning 6. Machine Learning in Anomaly Detection Systems 7. Detecting Advanced Persistent Threats 8. Evading Intrusion Detection Systems 9. Bypassing Machine Learning Malware Detectors 10. Best Practices for Machine Learning and Feature Engineering 11. Assessments 12. Other Books You May Enjoy

Building a botnet detector model with multiple machine learning techniques

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...

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