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Machine Learning for Cybersecurity Cookbook

You're reading from Machine Learning for Cybersecurity Cookbook Over 80 recipes on how to implement machine learning algorithms for building security systems using Python

Product type Paperback

Published in Nov 2019

Publisher Packt

ISBN-13 9781789614671

Length 346 pages

Edition 1st Edition

Languages

Python

Tools

Metasploit

Concepts

Cybersecurity

Author (1):

Emmanuel Tsukerman

View More author details

Table of Contents (11) Chapters

Preface

1. Machine Learning for Cybersecurity

2. Machine Learning-Based Malware Detection FREE CHAPTER

3. Advanced Malware Detection

4. Machine Learning for Social Engineering

5. Penetration Testing Using Machine Learning

6. Automatic Intrusion Detection

7. Securing and Attacking Data with Machine Learning

8. Secure and Private AI

9. Other Books You May Enjoy

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Appendix

Fake review generator

An important part of social engineering is impersonation. A social engineer may want to pretend to represent a company or business that doesn't currently exist. By creating a profile and populating it with convincing reviews, the social engineer can add credibility to the fake business. In this recipe, we show how to train an RNN so that it can generate new reviews, similar to the ones in the training dataset.

Training a fake review generator

Our first step is to train the model. Later, we will utilize it to produce new reviews.

Getting ready

...

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Authors (1)

Emmanuel Tsukerman

Emmanuel Tsukerman

Emmanuel Tsukerman graduated from Stanford University and obtained his Ph.D. from UC Berkeley. In 2017, Dr. Tsukerman's anti-ransomware product was listed in the Top 10 ransomware products of 2018 by PC Magazine. In 2018, he designed an ML-based, instant-verdict malware detection system for Palo Alto Networks' WildFire service of over 30,000 customers. In 2019, Dr. Tsukerman launched the first cybersecurity data science course.

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