10 Machine Learning Blueprints You Should Know for Cybersecurity

This is the code repository for 10 Machine Learning Blueprints You Should Know for Cybersecurity, published by Packt.

Protect your systems and boost your defenses with cutting-edge AI techniques

What is this book about?

This book covers the following exciting features: Use GNNs to build feature-rich graphs for bot detection and engineer graph-powered embeddings and features Discover how to apply ML techniques in the cybersecurity domain Apply state-of-the-art algorithms such as transformers and GNNs to solve security-related issues Leverage ML to solve modern security issues such as deep fake detection, machine-generated text identification, and stylometric analysis Apply privacy-preserving ML techniques and use differential privacy to protect user data while training ML models Build your own portfolio with end-to-end ML projects for cybersecurity

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:

import pandas as pd
import numpy as np
import os
from requests import get
}

Following is what you need for this book: This book is for machine learning practitioners interested in applying their skills to solve cybersecurity issues. Cybersecurity workers looking to leverage ML methods will also find this book useful. An understanding of the fundamental machine learning concepts and beginner-level knowledge of Python programming are needed to grasp the concepts in this book. Whether you’re a beginner or an experienced professional, this book offers a unique and valuable learning experience that’ll help you develop the skills needed to protect your network and data against the ever-evolving threat landscape.

With the following software and hardware list you can run all code files present in the book (Chapter 2-11).

Software and Hardware List

Chapter	Software required	OS required
2-11	PyTorch	Windows, Mac OS X, and Linux
2-11	TensorFlow	Windows, Mac OS X, and Linux
2-11	Keras	Windows, Mac OS X, and Linux
2-11	Scikit-learn	Windows, Mac OS X, and Linux
2-11	Miscellaneous – other libraries	Windows, Mac OS X, and Linux

Get to Know the Author

Rajvardhan Oak is a cybersecurity expert and researcher passionate about making the Internet a safer place for everyone. His research is focused on using machine learning to solve problems in computer security such as malware, botnets, reputation manipulation, and fake news. He obtained his bachelor's degree from the University of Pune, India, and his master's degree from the University of California, Berkeley. He has been invited to deliver training sessions at summits by the NSF and has served on the program committees of multiple technical conferences. His work has been featured by prominent news outlets such as WIRED magazine and the Daily Mail. In 2022, he received the ISC2 Global Achievement Award for Excellence in Cybersecurity, and in 2023, the honorary Doktor der Akademie from the Akademie für Hochschulbildung, Switzerland. He is based in Seattle and works as an applied scientist in the ads fraud division for Microsoft .

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