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)

Penetration Testing Using Machine Learning

A penetration test, aka a pen test, is an authorized simulated cyberattack on an information system, designed to elicit security vulnerabilities. In this chapter, we will be covering a wide selection of machine learning-technologies for penetration testing and security countermeasures. We'll begin by cracking a simple CAPTCHA system. We'll cover the automatic discovery of software vulnerabilities using deep learning, using fuzzing and code gadgets. We'll demonstrate enhancements to Metasploit, as well as covering how to assess the robustness of machine learning systems to adversarial attacks. Finally, we'll cover more specialized topics, such as deanonymizing Tor traffic, recognizing unauthorized access via keystroke dynamics, and detecting malicious URLs.

This chapter covers the following recipes:

  • CAPTCHA breaker...