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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

Techniques for authorship attribution

The previous section described the importance of authorship attribution and obfuscation. This section will focus on the attribution aspect—how we can design and build models to pinpoint the author of a given text.

Dataset

There has been prior research in the field of authorship attribution and obfuscation. The standard dataset for benchmarking on this task is the Brennan-Greenstadt Corpus. This dataset was collected through a survey at a university in the United States. 12 authors were recruited, and each author was required to submit a pre-written text that comprised at least 5,000 words.

A modified and improved version of this data—called the Extended Brennan-Greenstadt Corpus—was released later by the same authors. To generate this dataset, the authors conducted a large-scale survey by recruiting participants from Amazon Mechanical Turk (MTurk). MTurk is a platform that allows researchers and scientists to conduct...

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