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)

Selecting the best N-grams

The number of different N-grams grows exponentially in N. Even for a fixed tiny N, such as N=3, there are 256x256x256=16,777,216 possible N-grams. This means that the number of N-grams features is impracticably large. Consequently, we must select a smaller subset of N-grams that will be of most value to our classifiers. In this section, we show three different methods for selecting the topmost informative N-grams.

Getting ready

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

pip install sklearn nltk

In addition, benign and malicious files have been provided for you in the PE Samples Dataset folder in the...