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10 Machine Learning Blueprints You Should Know for Cybersecurity

You're reading from  10 Machine Learning Blueprints You Should Know for Cybersecurity

Product type Book
Published in May 2023
Publisher Packt
ISBN-13 9781804619476
Pages 330 pages
Edition 1st Edition
Languages
Author (1):
Rajvardhan Oak Rajvardhan Oak
Profile icon Rajvardhan Oak

Table of Contents (15) Chapters

Preface 1. Chapter 1: On Cybersecurity and Machine Learning 2. Chapter 2: Detecting Suspicious Activity 3. Chapter 3: Malware Detection Using Transformers and BERT 4. Chapter 4: Detecting Fake Reviews 5. Chapter 5: Detecting Deepfakes 6. Chapter 6: Detecting Machine-Generated Text 7. Chapter 7: Attributing Authorship and How to Evade It 8. Chapter 8: Detecting Fake News with Graph Neural Networks 9. Chapter 9: Attacking Models with Adversarial Machine Learning 10. Chapter 10: Protecting User Privacy with Differential Privacy 11. Chapter 11: Protecting User Privacy with Federated Machine Learning 12. Chapter 12: Breaking into the Sec-ML Industry 13. Index 14. Other Books You May Enjoy

Differentially private machine learning

In this section, we will look at how a fraud detection model can incorporate differential privacy. We will first look at the library we use to implement differential privacy, followed by how a credit card fraud detection machine learning model can be made differentially private.

IBM Diffprivlib

Diffprivlib is an open source Python library that provides a range of differential privacy tools and algorithms for data analysis. The library is designed to help data scientists and developers apply differential privacy techniques to their data in a simple and efficient way.

One of the key features of Diffprivlib is its extensive range of differentially private mechanisms. These include mechanisms for adding noise to data, such as the Gaussian, Laplace, and Exponential mechanisms, as well as more advanced mechanisms, such as the hierarchical and subsample mechanisms. The library also includes tools for calculating differential privacy parameters...

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