Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Update README.md #9

Open
wants to merge 2 commits into
base: master
Choose a base branch
from
Open

Update README.md #9

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
9407e3b
Update README.md
gabrielmahia Aug 1, 2020
db01cd5
Update README.md
gabrielmahia Aug 1, 2020
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
72 changes: 2 additions & 70 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,70 +1,2 @@
# Machine Learning for Cybersecurity Cookbook

<a href="https://www.packtpub.com/security/machine-learning-for-cybersecurity-cookbook?utm_source=github&utm_medium=repository&utm_campaign=9781789614671"><img src="https://www.packtpub.com/media/catalog/product/cache/e4d64343b1bc593f1c5348fe05efa4a6/9/7/9781789614671-original.jpeg" alt="Machine Learning for Cybersecurity Cookbook " height="256px" align="right"></a>

This is the code repository for [Machine Learning for Cybersecurity Cookbook ](https://www.packtpub.com/security/machine-learning-for-cybersecurity-cookbook?utm_source=github&utm_medium=repository&utm_campaign=9781789614671), published by Packt.

**Implement smart AI systems for preventing cyber attacks and detecting threats and network anomalies**

## What is this book about?
Organizations today face a major threat in terms of cybersecurity, from malicious URLs to credential reuse, and having robust security systems can make all the difference. With this book, you'll learn how to use Python libraries such as TensorFlow and scikit-learn to implement the latest artificial intelligence (AI) techniques and handle challenges faced by cybersecurity researchers.


This book covers the following exciting features:
Learn how to build malware classifiers to detect suspicious activities
Apply ML to generate custom malware to pentest your security
Use ML algorithms with complex datasets to implement cybersecurity concepts
Create neural networks to identify fake videos and images
Secure your organization from one of the most popular threats – insider threats
Defend against zero-day threats by constructing an anomaly detection system
Detect web vulnerabilities effectively by combining Metasploit and ML
Understand how to train a model without exposing the training data

If you feel this book is for you, get your [copy](https://www.amazon.com/dp/1789614678) today!

<a href="https://www.packtpub.com/?utm_source=github&utm_medium=banner&utm_campaign=GitHubBanner"><img src="https://raw.githubusercontent.com/PacktPublishing/GitHub/master/GitHub.png"
alt="https://www.packtpub.com/" border="5" /></a>

## Instructions and Navigations
All of the code is organized into folders. For example, Chapter02.

The code will look like the following:
```
from sklearn.model_selection import train_test_split
import pandas as pd
```

**Following is what you need for this book:**
If you’re a cybersecurity professional or ethical hacker who wants to build intelligent systems using the power of machine learning and AI, you’ll find this book useful. Familiarity with cybersecurity concepts and knowledge of Python programming is essential to get the most out of this book.

With the following software and hardware list you can run all code files present in the book (Chapter 1-8).
### Software and Hardware List
| Chapter | Software required | OS required |
| -------- | ------------------------------------ | ----------------------------------- |
| 1 | Python Environment (version depends on recipe) | Windows, Mac OS X, and Linux (Any) |
| 2 | Cuckoo Sandbox (latest) | Windows, Mac OS X, and Linux (Any) |
| 3 | UPX Packer 3.95 | Windows, Mac OS X, and Linux (Any) |
| 5 | Kali Linux 2019.3 | Windows, Mac OS X, and Linux (Any) |
| 6 | Wireshark 3.0.6 | Windows, Mac OS X, and Linux (Any) |
| 7 | Octave (latest) | Windows, Mac OS X, and Linux (Any) |
| Appendix | VirtualBox (latest) | 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://static.packt-cdn.com/downloads/9781789614671_ColorImages.pdf).

### Related products
* Hands-On Machine Learning for Cybersecurity [[Packt]](https://www.packtpub.com/in/big-data-and-business-intelligence/hands-machine-learning-cybersecurity?utm_source=github&utm_medium=repository&utm_campaign=9781788992282) [[Amazon]](https://www.amazon.com/dp/1788992288)

* Hands-On Artificial Intelligence for Cybersecurity [[Packt]](https://www.packtpub.com/in/data/hands-on-artificial-intelligence-for-cybersecurity?utm_source=github&utm_medium=repository&utm_campaign=9781789804027) [[Amazon]](https://www.amazon.com/dp/1789804027)


## Get to Know the Author
**Emmanuel Tsukerman** graduated from Stanford University and obtained his Ph.D. from UC Berkeley. In 2017, Dr. Tsukerman's anti-ransomware product was listed in the Top 10 ransomware products of 2018 by PC Magazine. In 2018, he designed an ML-based, instant-verdict malware detection system for Palo Alto Networks' WildFire service of over 30,000 customers. In 2019, Dr. Tsukerman launched the first cybersecurity data science course.




### Suggestions and Feedback
[Click here](https://docs.google.com/forms/d/e/1FAIpQLSdy7dATC6QmEL81FIUuymZ0Wy9vH1jHkvpY57OiMeKGqib_Ow/viewform) if you have any feedback or suggestions.


# Machine Learning for Cybersecurity
Machine Learning (ML) algorithms to curb cybersecurity threats