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)

Insider threat detection

Insider threat is a complex and growing challenge for employers. It is generally defined as any actions taken by an employee that are potentially harmful to the organization. These can include actions such as unsanctioned data transfer or the sabotaging of resources. Insider threats may manifest in various and novel forms motivated by differing goals, ranging from a disgruntled employee subverting the prestige of an employer, to advanced persistent threats (APT).

The insider risk database of the CERT Program of the Carnegie Mellon University Software Engineering Institute contains the largest public archive of red team scenarios. The simulation is built by combining real-world insider risk case studies with actual neutral clients secretly obtained from a defense corporation. The dataset represents months of traffic in a single engineering company from...