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)

Securing and Attacking Data with Machine Learning

In this chapter, we will learn how to employ machine learning (ML) to secure and attack data. We will cover how to assess the strength of a password using ML, and conversely, how to crack passwords using deep learning. Similarly, we will cover how to hide messages in plain sight using steganography, as well as how to detect steganography using ML. In addition, we will apply ML with hardware security to attack physically unclonable functions (PUFs) using AI.

In this chapter, we will cover the following recipes:

  • Assessing password security using ML
  • Deep learning for password cracking
  • Deep steganography
  • ML-based steganalysis
  • ML attacks on PUFs
  • Encryption using deep learning
  • HIPAA data breaches data exploration and visualization