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Mastering Machine Learning for Penetration Testing

You're reading from Mastering Machine Learning for Penetration Testing Develop an extensive skill set to break self-learning systems using Python

Product type Paperback

Published in Jun 2018

Publisher Packt

ISBN-13 9781788997409

Length 276 pages

Edition 1st Edition

Languages

Python

Concepts

Machine Learning

Author (1):

Chiheb Chebbi

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Table of Contents (13) Chapters

Preface

1. Introduction to Machine Learning in Pentesting FREE CHAPTER

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

What is the difference between MLP networks and deep learning networks?
Why DL recently is taking off?
Why do we need to iterate multiple times through different models?
What type of DL needed to translate English to French language?
Why malware visualization is a good method to classify malware?
What is the role of an activation function?
Can you mention three DL architectures?

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Authors (1)

Chiheb Chebbi

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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