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Mastering Machine Learning for Penetration Testing
Mastering Machine Learning for Penetration Testing

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

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Mastering Machine Learning for Penetration Testing

Chapter 2. 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 regression
    • Phishing detection with decision trees
    • Spam email detection with natural language processing (NLP...

Technical requirements


In this chapter, we are going to use the following Python libraries:

  • scikit-learn Python (≥ 2.7 or ≥ 3.3)
  • NumPy  (≥ 1.8.2)
  • NLTK

If you have not installed them yet, please make sure that they are installed before moving forward with this chapter. You can find the code files at https://github.com/PacktPublishing/Mastering-Machine-Learning-for-Penetration-Testing/tree/master/Chapter02.

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 goes through three steps: 

  1. Pre-engagement process: Preparing the social engineering operation
  2. During-engagement process: The engagement occurs
  3. Post-engagement process: Delivering a report

There are many social engineering techniques used by criminals:

  • Baiting: Convincing...

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:

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


In the next sections, we are going to learn how to build machine learning phishing detectors. We will cover the following two methods:

  • Phishing detection with logistic regression
  • Phishing detection with decision trees

Phishing detection with logistic regression

In this section, we are going to build a phishing detector from scratch with a logistic regression algorithm. Logistic regression is a well-known statistical technique used to make binomial predictions (two classes).

Like in every machine learning project, we will need data to feed our machine learning model. For our model, we are going to use the UCI Machine Learning Repository (Phishing Websites Data Set). You can check it out at https://archive.ics.uci.edu/ml/datasets/Phishing+Websites:

The dataset is provided as an arff file:

The following is a snapshot from the dataset:

For better manipulation, we have organized the dataset into a csv file:

As you probably...

NLP in-depth overview


NLP is the art of analyzing and understanding human languages by machines. According to many studies, more than 75% of the used data is unstructured. Unstructured data does not have a predefined data model or not organized in a predefined manner. Emails, tweets, daily messages and even our recorded speeches are forms of unstructured data. NLP is a way for machines to analyze, understand, and derive meaning from natural language. NLP is widely used in many fields and applications, such as:

  • Real-time translation
  • Automatic summarization
  • Sentiment analysis
  • Speech recognition
  • Build chatbots

Generally, there are two different components of NLP:

  • Natural Language Understanding (NLU): This refers to mapping input into a useful representation.
  • Natural Language Generation (NLG): This refers to transforming internal representations into useful representations. In other words, it is transforming data into written or spoken narrative. Written analysis for business intelligence dashboards...

Summary


In this chapter, we learned to detect phishing attempts by building three different projects from scratch. First, we discovered how to develop a phishing detector using two different machine learning techniques, thanks to cutting-edge Python machine learning libraries. The third project was a spam filter, based on NLP and Naive Bayes classification. In the next chapter, we will build various projects to detect malware, using different techniques and Python machine learning libraries.

Questions


We hope it was easy to go through this chapter. Now, as usual, it is practice time. Your job is to try building your own spam detection system. We will guide you through the questions.

In this chapter's GitHub repository, you will find a dataset collected from research done by Androutsopoulos, J. Koutsias, K.V. Chandrinos, George Paliouras, and C.D. Spyropoulos: An Evaluation of Naive Bayesian Anti-Spam Filtering. Proceedings of the workshop on Machine Learning in the New Information Age, G. Potamias, V. Moustakis and M. van Someren (eds.), 11th European Conference on Machine Learning, Barcelona, Spain, pp. 9-17, 2000.

You can now prepare the data:

  1. The following are some text-cleaning tasks to perform:
    • Clean your texts of stopwords, digits, and punctuation marks.
    • Perform lemmatization.
  2. Create a word dictionary, including their frequencies.

Note

In email texts, you will notice that the first line is the subject of the email and the third line is the body of the email (we only need the...

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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.

Who is this book for?

This book is for pen testers and security professionals who are interested in learning techniques to break an intelligent security system. Basic knowledge of Python is needed, but no prior knowledge of machine learning is necessary.

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

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Publication date : Jun 27, 2018
Length: 276 pages
Edition : 1st
Language : English
ISBN-13 : 9781788993111
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Product Details

Publication date : Jun 27, 2018
Length: 276 pages
Edition : 1st
Language : English
ISBN-13 : 9781788993111
Category :

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Table of Contents

10 Chapters
Introduction to Machine Learning in Pentesting Chevron down icon Chevron up icon
Phishing Domain Detection Chevron down icon Chevron up icon
Malware Detection with API Calls and PE Headers Chevron down icon Chevron up icon
Malware Detection with Deep Learning Chevron down icon Chevron up icon
Botnet Detection with Machine Learning Chevron down icon Chevron up icon
Machine Learning in Anomaly Detection Systems Chevron down icon Chevron up icon
Detecting Advanced Persistent Threats Chevron down icon Chevron up icon
Evading Intrusion Detection Systems Chevron down icon Chevron up icon
Bypassing Machine Learning Malware Detectors Chevron down icon Chevron up icon
Best Practices for Machine Learning and Feature Engineering Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
(4 Ratings)
5 star 50%
4 star 25%
3 star 0%
2 star 25%
1 star 0%
Houssem Dellai Jul 23, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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 Amazon
priyabrata mohanty Jan 13, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Gives you a good guidance on where to start.
Amazon Verified review Amazon
Alex Oct 31, 2018
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
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.
Amazon Verified review Amazon
sipy Aug 14, 2018
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
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.
Amazon Verified review Amazon
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