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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

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788997409
Length 276 pages
Edition 1st Edition
Languages
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Author (1):
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Chiheb Chebbi Chiheb Chebbi
Author Profile Icon Chiheb Chebbi
Chiheb Chebbi
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Table of Contents (13) Chapters Close

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

Social engineering overview

Social engineering, by definition, is the psychological manipulation of a person to get useful and sensitive information from them, which can later be used to compromise a system. In other words, criminals use social engineering to gain confidential information from people, by taking advantage of human behavior.

Social Engineering Engagement Framework

The Social Engineering Engagement Framework (SEEF) is a framework developed by Dominique C. Brack and Alexander Bahmram. It summarizes years of experience in information security and defending against social engineering. The stakeholders of the framework are organizations, governments, and individuals (personals). Social engineering engagement management...

You have been reading a chapter from
Mastering Machine Learning for Penetration Testing
Published in: Jun 2018
Publisher: Packt
ISBN-13: 9781788997409
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