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)

Tackling class imbalance

Often in applying machine learning to cybersecurity, we are faced with highly imbalanced datasets. For instance, it may be much easier to access a large collection of benign samples than it is to collect malicious samples. Conversely, you may be working at an enterprise that, for legal reasons, is prohibited from saving benign samples. In either case, your dataset will be highly skewed toward one class. As a consequence, naive machine learning aimed at maximizing accuracy will result in a classifier that predicts almost all samples as coming from the overrepresented class. There are several techniques that can be used to tackle the challenge of class imbalance.

Getting ready

Preparation for this...