10 Machine Learning Blueprints You Should Know for Cybersecurity: Protect your systems and boost your defenses with cutting-edge AI techniques

Work on 10 practical projects, each with a blueprint for a different machine learning technique, and apply them in the real world to fight against cybercrime

Purchase of the print or Kindle book includes a free PDF eBook

Key Features

• Learn how to frame a cyber security problem as a machine learning problem
• Examine your model for robustness against adversarial machine learning
• Build your portfolio, enhance your resume, and ace interviews to become a cybersecurity data scientist

Book Description

Machine learning in security is harder than other domains because of the changing nature and abilities of adversaries, high stakes, and a lack of ground-truth data. This book will prepare machine learning practitioners to effectively handle tasks in the challenging yet exciting cybersecurity space.

The book begins by helping you understand how advanced ML algorithms work and shows you practical examples of how they can be applied to security-specific problems with Python – by using open source datasets or instructing you to create your own. In one exercise, you’ll also use GPT 3.5, the secret sauce behind ChatGPT, to generate an artificial dataset of fabricated news. Later, you’ll find out how to apply the expert knowledge and human-in-the-loop decision-making that is necessary in the cybersecurity space. This book is designed to address the lack of proper resources available for individuals interested in transitioning into a data scientist role in cybersecurity. It concludes with case studies, interview questions, and blueprints for four projects that you can use to enhance your portfolio.

By the end of this book, you’ll be able to apply machine learning algorithms to detect malware, fake news, deep fakes, and more, along with implementing privacy-preserving machine learning techniques such as differentially private ML.

What you will learn

• Use GNNs to build feature-rich graphs for bot detection and engineer graph-powered embeddings and features
• Discover how to apply ML techniques in the cybersecurity domain
• Apply state-of-the-art algorithms such as transformers and GNNs to solve security-related issues
• Leverage ML to solve modern security issues such as deep fake detection, machine-generated text identification, and stylometric analysis
• Apply privacy-preserving ML techniques and use differential privacy to protect user data while training ML models
• Build your own portfolio with end-to-end ML projects for cybersecurity

Who this book is for

This book is for machine learning practitioners interested in applying their skills to solve cybersecurity issues. Cybersecurity workers looking to leverage ML methods will also find this book useful. An understanding of the fundamental machine learning concepts and beginner-level knowledge of Python programming are needed to grasp the concepts in this book. Whether you’re a beginner or an experienced professional, this book offers a unique and valuable learning experience that’ll help you develop the skills needed to protect your network and data against the ever-evolving threat landscape.

Table of Contents

1. On Cybersecurity and Machine Learning
2. Detecting Suspicious Activity
3. Malware Detection Using Transformers and BERT
4. Detecting Fake Reviews
5. Detecting Deepfakes
6. Detecting Machine-Generated Text
7. Attributing Authorship and How to Evade it
8. Detecting Fake News with Graph Neural Networks
9. Attacking Models with Adversarial Machine Learning
10. Protecting User Privacy with Differential Privacy
11. Protecting User Privacy with Federated Machine Learning
12. Breaking into the Sec-ML Industry

Rajvardhan Oak is a cybersecurity expert, researcher, and scientist with a focus on machine learning solutions to security issues such as fake news, malware, and botnets. He obtained his bachelor's degree from the University of Pune, India, and his master's degree from the University of California, Berkeley. He has served on the editorial committees of multiple technical conferences and journals. His work has been featured by prominent news outlets such as WIRED magazine and the Daily Mail. In 2022, he received the ISC2 Global Achievement Award for Excellence in Cybersecurity. He is based in the Seattle area and works for Microsoft as an applied scientist in the ads fraud division.