Machine Learning for Cybersecurity Cookbook, published by Packt
|size (curr.)||566006 kB|
Machine Learning for Cybersecurity Cookbook
This is the code repository for Machine Learning for Cybersecurity Cookbook , 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 today!
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.
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
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