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)

Automatic Intrusion Detection

An intrusion detection system monitors a network or a collection of systems for malicious activity or policy violations. Any malicious activity or violation caught is stopped or reported. In this chapter, we will design and implement several intrusion detection systems using machine learning. We will begin with the classical problem of detecting spam email. We will then move on to classifying malicious URLs. We will take a brief detour to explain how to capture network traffic, so that we may tackle more challenging network problems, such as botnet and DDoS detection. We will construct a classifier for insider threats. Finally, we will address the example-dependent, cost-sensitive, radically imbalanced, and challenging problem of credit card fraud.

This chapter contains the following recipes:

  • Spam filtering using machine learning
  • Phishing URL detection...