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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

Threat hunting with the ELK Stack

You have now seen a clear overview of the most important terminologies in threat hunting. So, let's build our threat-hunting platform. In the following sections, we will learn how to build a threat-hunting system by using open-source projects. In our hands-on guide, we will use one of the most promising solutions available—the ELK Stack. It includes three open-source projects, and is one of the most downloaded log management platforms nowadays.

The ELK Stack is widely used in many fields, including:

  • Business intelligence
  • Web analytics
  • Information security
  • Compliance

The ELK Stack is composed of the following components:

  • Elasticsearch: To search and analyze data
  • Logstash: To collect and transform data
  • Kibana: To visualize data

The following diagram illustrates the major components in the ELK Stack:

So, according to the main architecture...

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