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)

HIPAA data breaches – data exploration and visualization

Data exploration is the initial step in data analysis, whereby visual exploration is used to understand a dataset and the characteristics of the data. Data visualization helps us understand the data by placing it in an optical context so that our powerful visual processing centers can quickly find patterns and correlations in the data.

In this recipe, you will explore and visualize a public domain dataset regarding breaches of HIPAA confidential information.

Getting ready

For this recipe, you will need to install pandas and sklearn in pip. Use the following code to do so:

pip install pandas sklearn

In addition, the HIPAA-breach-report-2009-to-2017.csv...