Book Image

Machine Learning for Cybersecurity Cookbook

By : Emmanuel Tsukerman
Book Image

Machine Learning for Cybersecurity Cookbook

By: Emmanuel Tsukerman

Overview of this book

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. You'll begin by exploring various machine learning (ML) techniques and tips for setting up a secure lab environment. Next, you'll implement key ML algorithms such as clustering, gradient boosting, random forest, and XGBoost. The book will guide you through constructing classifiers and features for malware, which you'll train and test on real samples. As you progress, you'll build self-learning, reliant systems to handle cybersecurity tasks such as identifying malicious URLs, spam email detection, intrusion detection, network protection, and tracking user and process behavior. Later, you'll apply generative adversarial networks (GANs) and autoencoders to advanced security tasks. Finally, you'll delve into secure and private AI to protect the privacy rights of consumers using your ML models. By the end of this book, you'll have the skills you need to tackle real-world problems faced in the cybersecurity domain using a recipe-based approach.
Table of Contents (11 chapters)


Deepfake is the technique of using a neural network to take a video or image, superimpose some content onto it, and make the result look realistic. For example, the technique can take a video of Alice saying she supports a movement, and then, replacing Alice with Bob, create a realistic-looking video of Bob saying he supports the movement. Clearly, this technique has deep implications on the trust we can place on videos and images, while also providing a useful tool for social engineers.

In this recipe, we use a Deepfake variant to take the image of the face of one target and realistically superimpose it onto the image of another target's face. The recipe is a refactored and simplified version of the code in the GitHub repository, wuhuikai/FaceSwap.

Getting ready