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-Based Malware Detection

In this chapter, we begin to get serious about applying data science to cybersecurity. We will begin by learning how to perform static and dynamic analysis on samples. Building on this knowledge, we will learn how to featurize samples in order to construct a dataset with informative features. The highlight of the chapter is learning how to build a static malware detector using the featurization skills we have learned. Finally, you will learn how to tackle important machine learning challenges that occur in the domain of cybersecurity, such as class imbalance and false positive rate (FPR) constraints.

The chapter covers the following recipes:

  • Malware static analysis
  • Malware dynamic analysis
  • Using machine learning to detect the file type
  • Measuring the similarity between two strings
  • Measuring the similarity between two files
  • Extracting N...