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 DDoS

DDoS, or Distributed Denial of Service, is an attack in which traffic from different sources floods a victim, resulting in service interruption. There are many types of DDoS attacks, falling under three general categories: application-level, protocol, and volumetric attacks. Much of the DDoS defense today is manual. Certain IP addresses or domains are identified and then blocked. As DDoS bots become more sophisticated, such approaches are becoming outdated. Machine learning offers a promising automated solution.

The dataset we will be working with is a subsampling of the CSE-CIC-IDS2018, CICIDS2017, and CIC DoS datasets (2017). It consists of 80% benign and 20% DDoS traffic, in order to represent a more realistic ratio of normal-to-DDoS traffic.

Getting ready