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)

Extracting N-grams quickly using the hash-gram algorithm

In this section, we demonstrate a technique for extracting the most frequent N-grams quickly and memory-efficiently. This allows us to make the challenges that come with the immense number of N-grams easier. The technique is called Hash-Grams, and relies on hashing the N-grams as they are extracted. A property of N-grams is that they follow a power law that ensures that hash collisions have an insignificant impact on the quality of the features thus obtained.

Getting ready

Preparation for this recipe involves installing nltk in pip. The command is as follows:

pip install nltk

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