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)

Detecting obfuscated JavaScript

In this section, we will see how to use machine learning to detect when a JavaScript file is obfuscated. Doing so can serve to create a binary feature, obfuscated or not, to be used in benign/malicious classification, and can serve also as a prerequisite step to deobfuscating the scripts.

Getting ready

Preparation for this recipe involves installing the scikit-learn package in pip. The command is as follows:

pip install sklearn

In addition, obfuscated and non-obfuscated JavaScript files have been provided for you in the repository. Extract JavascriptSamplesNotObfuscated.7z to a folder named JavaScript Samples. Extract JavascriptSamplesObfuscated.7z to a folder named JavaScript Samples...