You're reading from Machine Learning Security Principles Keep data, networks, users, and applications safe from prying eyes

Product type Paperback

Published in Dec 2022

Publisher Packt

ISBN-13 9781804618851

Length 450 pages

Edition 1st Edition

Languages

Python

Tools

Google cloud buildpack

Concepts

Machine Learning

Author (1):

John Paul Mueller

View More author details

Table of Contents (19) Chapters

Preface

1. Part 1 – Securing a Machine Learning System

2. Chapter 1: Defining Machine Learning Security FREE CHAPTER

3. Chapter 2: Mitigating Risk at Training by Validating and Maintaining Datasets

4. Chapter 3: Mitigating Inference Risk by Avoiding Adversarial Machine Learning Attacks

5. Part 2 – Creating a Secure System Using ML

6. Chapter 4: Considering the Threat Environment

7. Chapter 5: Keeping Your Network Clean

8. Chapter 6: Detecting and Analyzing Anomalies

9. Chapter 7: Dealing with Malware

10. Chapter 8: Locating Potential Fraud

11. Chapter 9: Defending against Hackers

12. Part 3 – Protecting against ML-Driven Attacks

13. Chapter 10: Considering the Ramifications of Deepfakes

14. Chapter 11: Leveraging Machine Learning for Hacking

15. Part 4 – Performing ML Tasks in an Ethical Manner

16. Chapter 12: Embracing and Incorporating Ethical Behavior

17. Index

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Building a fraud detection example

This section will show you how to build a simple fraud detection example using real sanitized credit card data available on Kaggle. The transactions occurred in September 2013 and there are 492 frauds out of 284,807 transactions, which is unbalanced because the number of frauds is a little low for training a model. The data has been transformed by Principal Component Analysis (PCA) using the techniques demonstrated in the Relying on Principle Component Analysis section of Chapter 6, Detecting and Analyzing Anomalies. Only the Amount column has the original value in it. The Class column has been added to label the data. You can also find the source code for this example in the MLSec; 08; Perform Fraud Detection.ipynb file of the downloadable source.