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)

Botnet traffic detection

A botnet is a network of internet-connected compromised devices. Botnets can be used to perform a distributed denial-of-service attack (DDoS attack), steal data, send spam, among many other creative malicious uses. Botnets can cause absurd amounts of damage. For example, a quick search for the word botnet on Google shows that 3 days before the time of writing, the Electrum Botnet Stole $4.6 Million in cryptocurrencies. In this recipe, we build a classifier to detect botnet traffic.

The dataset used is a processed subset of a dataset called CTU-13, and consists of botnet traffic captured in Czechia, at the CTU University in 2011. The dataset is a large capture of real botnet traffic mixed with normal and background traffic.

Getting ready