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 packed malware

Packing is the compression or encryption of an executable file, distinguished from ordinary compression in that it is typically decompressed during runtime, in memory, as opposed to being decompressed to disk, prior to execution. Packers pose an obfuscation challenge to analysts.

A packer called VMProtect, for example, protects its content from analyst eyes by executing in a virtual environment with a unique architecture, making it a great challenge for anyone to analyze the software.

Amber is a reflective PE packer for bypassing security products and mitigations. It can pack regularly compiled PE files into reflective payloads that can load and execute themselves like a shellcode. It enables stealthy in-memory payload deployment that can be used to bypass anti-virus, firewall, IDS, IPS products, and application whitelisting mitigations. The most commonly...