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Phishing Domain Detection

Social engineering is one of the most dangerous threats facing every individual and modern organization. Phishing is a well-known, computer-based, social engineering technique. Attackers use disguised email addresses as a weapon to target large companies. With the huge number of phishing emails received every day, companies are not able to detect all of them. That is why new techniques and safeguards are needed to defend against phishing. This chapter will present the steps required to build three different machine learning-based projects to detect phishing attempts, using cutting-edge Python machine learning libraries.

In this chapter, we will cover:

A social engineering overview
The steps for social engineering penetration testing
Building a real-time phishing attack detector using different machine learning models:
- Phishing detection with logistic...

Key benefits

• Identify ambiguities and breach intelligent security systems

• Perform unique cyber attacks to breach robust systems

• Learn to leverage machine learning algorithms

Description

Cyber security is crucial for both businesses and individuals. As systems are getting smarter, we now see machine learning interrupting computer security. With the adoption of machine learning in upcoming security products, it’s important for pentesters and security researchers to understand how these systems work, and to breach them for testing purposes. This book begins with the basics of machine learning and the algorithms used to build robust systems. Once you’ve gained a fair understanding of how security products leverage machine learning, you'll dive into the core concepts of breaching such systems. Through practical use cases, you’ll see how to find loopholes and surpass a self-learning security system. As you make your way through the chapters, you’ll focus on topics such as network intrusion detection and AV and IDS evasion. We’ll also cover the best practices when identifying ambiguities, and extensive techniques to breach an intelligent system. By the end of this book, you will be well-versed with identifying loopholes in a self-learning security system and will be able to efficiently breach a machine learning system.

What you will learn

•Take an in-depth look at machine learning

•Get to know natural language processing (NLP)

•Understand malware feature engineering

•Build generative adversarial networks using Python libraries

•Work on threat hunting with machine learning and the ELK stack

•Explore the best practices for machine learning

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

Frequently bought together

Hands-On Machine Learning for Cybersecurity

€36.99

€41.99

Mastering Machine Learning for Penetration Testing

€32.99

Total € 111.97

Houssem Dellai Jul 23, 2018

Before reqding a book, I search for the qualifications of the author. And clearly here he shows up his motivation and willing to share through international conferences and his second book !

Amazon Verified review

priyabrata mohanty Jan 13, 2019

Gives you a good guidance on where to start.

Alex Oct 31, 2018

For me, this was a good introduction to Machine Learning with regards to Pentesting. Some interesting concepts and just enough information to wet your appetite to look further into ML. Recommend it for anyone with an interest to get a good base to start at.

sipy Aug 14, 2018

While this book does an excellent job of describing many aspects of Machine Learning, it doesn't live up to its title of showing you how to apply M.L. techniques to Penetration Testing.**HOWEVER** If this book was retitled: "M.L. for Dummies", I would give it 5 stars!!! Note that I'm not mocking the author or potential reader - it really is an **excellent** introduction for newcomers to the M.L. field - exactly what the "...for Dummies" book series is intended to be!The vast majority of this book is spent describing concepts and algorithms for M.L., which I feel the author is very technically qualified to teach. His writing style is very approachable, and he is very, very adept at relating these complicated topics to newcomers in the M.L. field. In fact, I don't recall another source that can convey, in so few words, these deeply technical thoughts as easily as this book.The closest thing in this book describing M.L. "for Pentesting" is probably the 40 pages of chapters 8 and 9, which mostly name-drop various tools that can be used to investigate perturbing training data to influence an M.L. model's classification capabilities. No mention is made in this section on how to use these tools and techniques for *Penetration* *Testing* - only for perturbing training data, which the author doesn't describe how to utilize for an actual Pentest-type of attack!Chapter 10 may be the most undersold chapter in this book. It covers best practices for Feature Engineering - something that you *must* master in order to use M.L. The lucid descriptions in this chapter, alone, almost justified my purchase price!Again, I think this is a great book written by a very competent author who has a gift for conveying very detailed technical matter in an easy-to-grasp way. His writing style is very approachable, and this book is an easy read for the newcomer to M.L.In fact, if you are new to M.L., I recommend reading this book, first, to see if it is even something that you may want to partake of.But - again - this book is *not* a "Machine Learning for Pentesting" book. It doesn't come close to hitting this mark, in my opinion.

Mastering Machine Learning for Penetration Testing: Develop an extensive skill set to break self-learning systems using Python

What do you get with Print?

Mastering Machine Learning for Penetration Testing

Phishing Domain Detection

Technical requirements

Social engineering overview

Social Engineering Engagement Framework

Steps of social engineering penetration testing

Building real-time phishing attack detectors using different machine learning models

Phishing detection with logistic regression

NLP in-depth overview

Summary

Questions

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Mastering Machine Learning for Penetration Testing: Develop an extensive skill set to break self-learning systems using Python

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Key benefits

Description

Who is this book for?

What you will learn

Product Details

What do you get with Print?

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