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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

View More author details

Table of Contents (13) Chapters

Preface

1. Introduction to Machine Learning in Pentesting

2. Phishing Domain Detection FREE CHAPTER

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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Steps of social engineering penetration testing

Penetration testing simulates a black hat hacker attack in order to evaluate the security posture of a company for deploying the required safeguard. Penetration testing is a methodological process, and it goes through well-defined steps. There are many types of penetration testing:

White box pentesting
Black box pentesting
Grey box pentesting

To perform a social engineering penetration test, you need to follow the following steps:

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Mastering Machine Learning for Penetration Testing

Published in: Jun 2018

Publisher: Packt

ISBN-13: 9781788997409

© 2018 Packt Publishing Limited All Rights Reserved

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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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