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Machine Learning for Cybersecurity Cookbook

You're reading from Machine Learning for Cybersecurity Cookbook

Product type Book

Published in Nov 2019

Publisher Packt

ISBN-13 9781789614671

Pages 346 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Author (1):

Emmanuel Tsukerman

Table of Contents (11) Chapters

Preface

1. Machine Learning for Cybersecurity

2. Machine Learning-Based Malware Detection

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

HIPAA data breaches – data exploration and visualization

Data exploration is the initial step in data analysis, whereby visual exploration is used to understand a dataset and the characteristics of the data. Data visualization helps us understand the data by placing it in an optical context so that our powerful visual processing centers can quickly find patterns and correlations in the data.

In this recipe, you will explore and visualize a public domain dataset regarding breaches of HIPAA confidential information.

Getting ready

For this recipe, you will need to install pandas and sklearn in pip. Use the following code to do so:

pip install pandas sklearn

In addition, the HIPAA-breach-report-2009-to-2017.csv...

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Authors (1)

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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