This is the code repository for Mastering Machine Learning for Penetration Testing, published by Packt.
Develop an extensive skill set to break self-learning systems using Python
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 covers the following exciting features:
- Take an in-depth look at machine learning
- Get to know natural language processing (NLP)
- Understand malware feature engineering
- Build generative adversarial networks using Python libraries
- Work on threat hunting with machine learning and the ELK stack
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
input {
file {
path => "/opt/bitnami/apache2/logs/access_log"
start_position => beginning
}
}
Following is what you need for this book: This book is for pen testers and security professionals who are interested in learning techniques to break an intelligent security system. Basic knowledge of Python is needed, but no prior knowledge of machine learning is necessary.
With the following software and hardware list you can run all code files present in the book (Chapter 1-10).
| Chapter | Software required | OS required |
|---|---|---|
| 1-10 | Kali Linux | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. [Click here to download it]( https://www.packtpub.com/sites/default/files/ downloads/MasteringMachineLearningforPenetrationTesting_ColorImages.pdf).
Chiheb Chebbi Chiheb Chebbi is an InfoSec enthusiast who has experience in various aspects of information security, focusing on the investigation of advanced cyber attacks and researching cyber espionage and APT attacks. Chiheb is currently pursuing an engineering degree in computer science at TEK-UP university in Tunisia.
His core interests are infrastructure penetration testing, deep learning, and malware analysis. In 2016, he was included in the Alibaba Security Research Center Hall Of Fame. His talk proposals were accepted by DeepSec 2017, Blackhat Europe 2016, and many world-class information security conferences.
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