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10 Machine Learning Blueprints You Should Know for Cybersecurity

10 Machine Learning Blueprints You Should Know for Cybersecurity

By : Rajvardhan Oak
4.7 (3)
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10 Machine Learning Blueprints You Should Know for Cybersecurity

10 Machine Learning Blueprints You Should Know for Cybersecurity

4.7 (3)
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)
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Summary

This chapter provided an introduction to malware and a hands-on blueprint for how it can be detected using transformers. First, we discussed the concepts of malware and the various forms they come in (rootkits, viruses, and worms). We then discussed the attention mechanism and transformer architecture, which are recent advances that have taken the machine learning world by storm. We also looked at BERT, a model that has beat several baselines in tasks such as sentence classification and question-answering. We leveraged BERT for malware detection by fine-tuning a pre-trained model on API call sequence data.

Malware is a pressing problem that places users of phones and computers at great risk. Data scientists and machine learning practitioners who are interested in the security space need to have a strong understanding of how malware works and the architecture of models that can be used for detection. This chapter provided all of the knowledge needed and is a must to master...

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