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)

Machine Learning for Social Engineering

There are a lot of cool new applications of machine learning (ML), and nowhere do these shine as much as they do in social engineering. ML has enabled hugely successful automated spear phishing, as we will learn via a Twitter spear phishing bot recipe. It has also been used to generate fake, but realistic, videos and, at the same time, to discover when these are fake. It offers the ability to voice transfer, detect lies, and many other handy tools that you will see in this chapter's recipes, designed to step up your social engineering game.

This chapter covers the following recipes:

  • Twitter spear phishing bot
  • Voice impersonation
  • Speech recognition for Open Source Intelligence (OSINT)
  • Facial recognition
  • Deepfake
  • Deepfake recognition
  • Lie detection using ML
  • Personality analysis
  • Social Mapper
  • Training a fake review generator
  • Generating...