Book Image

10 Machine Learning Blueprints You Should Know for Cybersecurity

By : Rajvardhan Oak
4 (1)
Book Image

10 Machine Learning Blueprints You Should Know for Cybersecurity

4 (1)
By: Rajvardhan Oak

Overview of this book

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.
Table of Contents (15 chapters)

Summary

In recent times, human reliance on ML has grown exponentially. ML models are involved in several security-critical applications such as fraud, abuse, and other kinds of cybercrime. However, many models are susceptible to adversarial attacks, where attackers manipulate the input so as to fool the model. This chapter covered the basics of AML and the goals and strategies that attackers employ. We then discussed two popular adversarial attack methods, FGSM and PGD, along with their implementation in Python. Next, we learned about methods for manipulating text and their implementation.

Because of the importance and prevalence of ML in our lives, it is necessary for security data scientists to understand adversarial attacks and learn to defend against them. This chapter provides a solid foundation for AML and the kinds of attacks involved.

So far, we have discussed multiple aspects of ML for security problems. In the next chapter, we will pivot to a closely related topic&...