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)

Featurizing PDF files

In this section, we will see how to featurize PDF files in order to use them for machine learning. The tool we will be utilizing is the PDFiD Python script designed by Didier Stevens ( Stevens selected a list of 20 features that are commonly found in malicious files, including whether the PDF file contains JavaScript or launches an automatic action. It is suspicious to find these features in a file, hence, the appearance of these can be indicative of malicious behavior.

Essentially, the tool scans through a PDF file, and counts the number of occurrences of each of the ~20 features. A run of the tool appears as follows:

 PDFiD 0.2.5 PythonBrochure.pdf

PDF Header: %PDF-1.6
obj 1096
endobj 1095
stream 1061
endstream 1061
xref 0
trailer 0