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Mastering Machine Learning for Penetration Testing

You're reading from Mastering Machine Learning for Penetration Testing

Product type Book

Published in Jun 2018

Publisher Packt

ISBN-13 9781788997409

Pages 276 pages

Edition 1st Edition

Languages

Concepts

Penetration Testing

Author (1):

Chiheb Chebbi

Table of Contents (13) Chapters

Preface

1. Introduction to Machine Learning in Pentesting

2. Phishing Domain Detection

3. Malware Detection with API Calls and PE Headers

4. Malware Detection with Deep Learning

5. Botnet Detection with Machine Learning

6. Machine Learning in Anomaly Detection Systems

7. Detecting Advanced Persistent Threats

8. Evading Intrusion Detection Systems

9. Bypassing Machine Learning Malware Detectors

10. Best Practices for Machine Learning and Feature Engineering

11. Assessments

12. Other Books You May Enjoy

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Questions

As we do after every chapter, we are going to give you the opportunity to practice what you learned and evaluate your skills. This chapter's GitHub repository contains a link to a botnet traffic dataset in the Practice folder:

Download the dataset and load it with the pandas library
Select suitable features
Identify the training and testing sets, then export them into a pickle file
Load the pickle file
Import a support vector machine classifier and fit the model
Train the SVM model
Print out the accuracy of the model built

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Authors (1)

Chiheb Chebbi

Chiheb Chebbi is an InfoSec enthusiast who has experience in various aspects of information security, focusing on the investigation of advanced cyber attacks and researching cyber espionage and APT attacks. Chiheb is currently pursuing an engineering degree in computer science at TEK-UP university in Tunisia. His core interests are infrastructure penetration testing, deep learning, and malware analysis. In 2016, he was included in the Alibaba Security Research Center Hall Of Fame. His talk proposals were accepted by DeepSec 2017, Blackhat Europe 2016, and many world-class information security conferences.

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