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.
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