PacktPublishing/Hands-on-Machine-Learning-for-Cyber-Security
Hands-On Machine Learning for Cybersecurity, published by Packt
| repo name | PacktPublishing/Hands-on-Machine-Learning-for-Cyber-Security |
| repo link | https://github.com/PacktPublishing/Hands-on-Machine-Learning-for-Cyber-Security |
| homepage | |
| language | Jupyter Notebook |
| size (curr.) | 16677 kB |
| stars (curr.) | 27 |
| created | 2018-12-31 |
| license | MIT License |
Hands-On Machine Learning for Cybersecurity
This is the code repository for Hands-On Machine Learning for Cybersecurity, published by Packt.
Safeguard your system by making your machines intelligent using the Python ecosystem
What is this book about?
Cyber threats today are one of the costliest losses that an organization can face. In this book, we use the most efficient tool to solve the big problems that exist in the cybersecurity domain.
This book covers the following exciting features:
- Use machine learning algorithms with complex datasets to implement cybersecurity concepts
- Implement machine learning algorithms such as clustering, k-means, and Naive Bayes to solve real-world problems
- Learn to speed up a system using Python libraries with NumPy, Scikit-learn, and CUDA
- Understand how to combat malware, detect spam, and fight financial fraud to mitigate cyber crimes
- Use TensorFlow in the cybersecurity domain and implement real-world examples
If you feel this book is for you, get your copy today!
Instructions and Navigations
All of the code is organized into folders.
The code will look like the following:
def url_has_exe(url):
if url.find('.exe')!=-1:
return 1
else :
return 0
Following is what you need for this book:
This book is for the data scientists, machine learning developers, security researchers, and anyone keen to apply machine learning to up-skill computer security. Having some working knowledge of Python and being familiar with the basics of machine learning and cybersecurity fundamentals will help to get the most out of the book
With the following software and hardware list you can run all code files present in the book (Chapter 1-15).
Software and Hardware List
| Chapter | Software required | OS required |
|---|---|---|
| 1-11 | Python 2.x/3.x | 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.
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Get to Know the Authors
Soma Halder is the data science lead of the big data analytics group at Reliance Jio Infocomm Ltd, one of India’s largest telecom companies. She specializes in analytics, big data, cybersecurity, and machine learning. She has approximately 10 years of machine learning experience, especially in the field of cybersecurity. She studied at the University of Alabama, Birmingham where she did her master’s with an emphasis on Knowledge discovery and Data Mining and computer forensics. She has worked for Visa, Salesforce, and AT&T. She has also worked for start-ups, both in India and the US (E8 Security, Headway ai, and Norah ai). She has several conference publications to her name in the field of cybersecurity, machine learning, and deep learning.
Sinan Ozdemir is a data scientist, start-up founder, and educator living in the San Francisco Bay Area. He studied pure mathematics at the Johns Hopkins University. He then spent several years conducting lectures on data science there, before founding his own start-up, Kylie ai, which uses artificial intelligence to clone brand personalities and automate customer service communications. He is also the author of Principles of Data Science, available through Packt.
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