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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

Selecting the best N-grams

The number of different N-grams grows exponentially in N. Even for a fixed tiny N, such as N=3, there are 256x256x256=16,777,216 possible N-grams. This means that the number of N-grams features is impracticably large. Consequently, we must select a smaller subset of N-grams that will be of most value to our classifiers. In this section, we show three different methods for selecting the topmost informative N-grams.

Getting ready

Preparation for this recipe consists of installing the scikit-learn and nltk packages in pip. The instructions are as follows:

pip install sklearn nltk

In addition, benign and malicious files have been provided for you in the PE Samples Dataset folder in the...

You have been reading a chapter from

Machine Learning for Cybersecurity Cookbook

Published in: Nov 2019

Publisher: Packt

ISBN-13: 9781789614671

© 2019 Packt Publishing Limited All Rights Reserved

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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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