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)

Building a static malware detector

In this section, we will see how to put together the recipes we discussed in prior sections to build a malware detector. Our malware detector will take in both features extracted from the PE header as well as features derived from N-grams.

Getting ready

Preparation for this recipe consists of installing the scikit-learn, nltk, and pefile packages in pip. The instructions are as follows:

pip install sklearn nltk pefile

In addition, benign and malicious files have been provided for you in the "PE Samples Dataset" folder in the root of the repository. Extract all archives named "Benign PE Samples*.7z" to a folder named "Benign PE Samples". Extract...