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

Modeling fake reviews with regression

In this section, we will use the features we examined to attempt to model our data with linear regression.

Ordinary Least Squares regression

Ordinary Least Squares (OLS) linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. The goal of OLS is to find the linear function that best fits the data by minimizing the sum of squared errors between the observed values and the predicted values of the dependent variable.

The linear function is typically expressed as:

Y = β 0+ β 1 X 1+ β 2 X 2+ ... + β n X n+ ε

where Y is the dependent variable, X1, X2, ..., Xn are the independent variables, β0, β1, β2, ..., βn are the coefficients (or parameters) that measure the effect of each independent variable on the dependent variable, and ε is the error term (or residual...

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