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Machine Learning for Cybersecurity Cookbook
Machine Learning for Cybersecurity Cookbook

Machine Learning for Cybersecurity Cookbook: Over 80 recipes on how to implement machine learning algorithms for building security systems using Python

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Machine Learning for Cybersecurity Cookbook

Machine Learning-Based Malware Detection

In this chapter, we begin to get serious about applying data science to cybersecurity. We will begin by learning how to perform static and dynamic analysis on samples. Building on this knowledge, we will learn how to featurize samples in order to construct a dataset with informative features. The highlight of the chapter is learning how to build a static malware detector using the featurization skills we have learned. Finally, you will learn how to tackle important machine learning challenges that occur in the domain of cybersecurity, such as class imbalance and false positive rate (FPR) constraints.

The chapter covers the following recipes:

  • Malware static analysis
  • Malware dynamic analysis
  • Using machine learning to detect the file type
  • Measuring the similarity between two strings
  • Measuring the similarity between two files
  • Extracting N...

Technical requirements

Malware static analysis

In static analysis, we examine a sample without executing it. The amount of information that can be obtained this way is large, ranging from something as simple as the name of the file to the more complex, such as specialized YARA signatures. We will be covering a selection of the large variety of features you could obtain by statically analyzing a sample. Despite its power and convenience, static analysis is no silver bullet, mainly because software can be obfuscated. For this reason, we will be employing dynamic analysis and other techniques in later chapters.

Computing the hash of a sample

Without delving into the intricacies of hashing, a hash is essentially a short and unique string signature....

Malware dynamic analysis

Unlike static analysis, dynamic analysis is a malware analysis technique in which the expert executes the sample, and then studies the sample's behavior as it is being run. The main advantage of dynamic analysis over static is that it allows you to bypass obfuscation by simply observing how a sample behaves, rather than trying to decipher the sample's contents and behavior. Since malware is intrinsically unsafe, researchers resort to executing samples in a virtual machine (VM). This is called sandboxing.

Getting ready

One of the most prominent tools for automating the analysis of samples in a VM is Cuckoo Sandbox. The initial installation of Cuckoo Sandbox is straightforward; simply run the...

Using machine learning to detect the file type

One of the techniques hackers use to sneak their malicious files into security systems is to obfuscate their file types. For example, a (malicious) PowerShell script is expected to have an extension, .ps1. A system administrator can aim to combat the execution of all PowerShell scripts on a system by preventing the execution of all files with the .ps1 extension. However, the mischievous hacker can remove or change the extension, rendering the file's identity a mystery. Only by examining the contents of the file can it then be distinguished from an ordinary text file. For practical reasons, it is not possible for humans to examine all text files on a system. Consequently, it is expedient to resort to automated methods. In this chapter, we will demonstrate how you can use machine learning to detect the file type...

Measuring the similarity between two strings

To check whether two files are identical, we utilize standard cryptographic hash functions, such as SHA256 and MD5. However, at times, we would like to also know to what extent two files are similar. For that purpose, we utilize similarity hashing algorithms. The one we will be demonstrating here is ssdeep.

First, let's see how to use ssdeep to compare two strings. This can be useful to detect tampering in a text or script and also plagiarism.

Getting ready

Preparation for this recipe consists of installing the ssdeep package in pip. The installation is a little tricky and does not always work on Windows. Instructions can be found at https://python-ssdeep.readthedocs.io...

Measuring the similarity between two files

Now, we are going to see how to apply ssdeep to measure the similarity between two binary files. The applications of this concept are many, but one in particular is using the similarity measure as a distance in clustering.

Getting ready

Preparation for this recipe consists of installing the ssdeep package in pip. The installation is a little tricky and does not always work on Windows. Instructions can be found at https://python-ssdeep.readthedocs.io/en/latest/installation.html.

If you only have a Windows machine and it does not work, then one possible solution is to run ssdeep on an Ubuntu VM by installing pip with this command:

pip install ssdeep

In addition, download a...

Extracting N-grams

In standard quantitative analysis of text, N-grams are sequences of N tokens (for example, words or characters). For instance, given the text The quick brown fox jumped over the lazy dog, if our tokens are words, then the 1-grams are the, quick, brown, fox, jumped, over, the, lazy, and dog. The 2-grams are the quick, quick brown, brown fox, and so on. The 3-grams are the quick brown, quick brown fox, brown fox jumped, and so on. Just like the local statistics of the text allowed us to build a Markov chain to perform statistical predictions and text generation from a corpus, N-grams allow us to model the local statistical properties of our corpus. Our ultimate goal is to utilize the counts of N-grams to help us predict whether a sample is malicious or benign. In this recipe, we demonstrate how to extract N-gram counts from a sample.

...

Selecting the best N-grams

The number of different N-grams grows exponentially in N. Even for a fixed tiny N, such as N=3, there are 256x256x256=16,777,216 possible N-grams. This means that the number of N-grams features is impracticably large. Consequently, we must select a smaller subset of N-grams that will be of most value to our classifiers. In this section, we show three different methods for selecting the topmost informative N-grams.

Getting ready

Preparation for this recipe consists of installing the scikit-learn and nltk packages in pip. The instructions are as follows:

pip install sklearn nltk

In addition, benign and malicious files have been provided for you in the PE Samples Dataset folder in the...

Building a static malware detector

In this section, we will see how to put together the recipes we discussed in prior sections to build a malware detector. Our malware detector will take in both features extracted from the PE header as well as features derived from N-grams.

Getting ready

Preparation for this recipe consists of installing the scikit-learn, nltk, and pefile packages in pip. The instructions are as follows:

pip install sklearn nltk pefile

In addition, benign and malicious files have been provided for you in the "PE Samples Dataset" folder in the root of the repository. Extract all archives named "Benign PE Samples*.7z" to a folder named "Benign PE Samples". Extract...

Tackling class imbalance

Often in applying machine learning to cybersecurity, we are faced with highly imbalanced datasets. For instance, it may be much easier to access a large collection of benign samples than it is to collect malicious samples. Conversely, you may be working at an enterprise that, for legal reasons, is prohibited from saving benign samples. In either case, your dataset will be highly skewed toward one class. As a consequence, naive machine learning aimed at maximizing accuracy will result in a classifier that predicts almost all samples as coming from the overrepresented class. There are several techniques that can be used to tackle the challenge of class imbalance.

Getting ready

Preparation for this...

Handling type I and type II errors

In many situations in machine learning, one type of error may be more important than another. For example, in a multilayered defense system, it may make sense to require a layer to have a low false alarm (low false positive) rate, at the cost of some detection rate. In this section, we provide a recipe for ensuring that the FPR does not exceed a desired limit by using thresholding.

Getting ready

Preparation for this recipe consists of installing scikit-learn and xgboost in pip. The instructions are as follows:

pip install sklearn xgboost

How to do it...

...
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Key benefits

  • Manage data of varying complexity to protect your system using the Python ecosystem
  • Apply ML to pentesting, malware, data privacy, intrusion detection system(IDS) and social engineering
  • Automate your daily workflow by addressing various security challenges using the recipes covered in the book

Description

Organizations today face a major threat in terms of cybersecurity, from malicious URLs to credential reuse, and having robust security systems can make all the difference. With this book, you'll learn how to use Python libraries such as TensorFlow and scikit-learn to implement the latest artificial intelligence (AI) techniques and handle challenges faced by cybersecurity researchers. You'll begin by exploring various machine learning (ML) techniques and tips for setting up a secure lab environment. Next, you'll implement key ML algorithms such as clustering, gradient boosting, random forest, and XGBoost. The book will guide you through constructing classifiers and features for malware, which you'll train and test on real samples. As you progress, you'll build self-learning, reliant systems to handle cybersecurity tasks such as identifying malicious URLs, spam email detection, intrusion detection, network protection, and tracking user and process behavior. Later, you'll apply generative adversarial networks (GANs) and autoencoders to advanced security tasks. Finally, you'll delve into secure and private AI to protect the privacy rights of consumers using your ML models. By the end of this book, you'll have the skills you need to tackle real-world problems faced in the cybersecurity domain using a recipe-based approach.

Who is this book for?

This book is for cybersecurity professionals and security researchers who are looking to implement the latest machine learning techniques to boost computer security, and gain insights into securing an organization using red and blue team ML. This recipe-based book will also be useful for data scientists and machine learning developers who want to experiment with smart techniques in the cybersecurity domain. Working knowledge of Python programming and familiarity with cybersecurity fundamentals will help you get the most out of this book.

What you will learn

  • Learn how to build malware classifiers to detect suspicious activities
  • Apply ML to generate custom malware to pentest your security
  • Use ML algorithms with complex datasets to implement cybersecurity concepts
  • Create neural networks to identify fake videos and images
  • Secure your organization from one of the most popular threats – insider threats
  • Defend against zero-day threats by constructing an anomaly detection system
  • Detect web vulnerabilities effectively by combining Metasploit and ML
  • Understand how to train a model without exposing the training data
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Table of Contents

9 Chapters
Machine Learning for Cybersecurity Chevron down icon Chevron up icon
Machine Learning-Based Malware Detection Chevron down icon Chevron up icon
Advanced Malware Detection Chevron down icon Chevron up icon
Machine Learning for Social Engineering Chevron down icon Chevron up icon
Penetration Testing Using Machine Learning Chevron down icon Chevron up icon
Automatic Intrusion Detection Chevron down icon Chevron up icon
Securing and Attacking Data with Machine Learning Chevron down icon Chevron up icon
Secure and Private AI Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
(2 Ratings)
5 star 50%
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1 star 50%
EnviroLaCiTY Apr 29, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
An incredible synopsis of the subject with some valuable data, a superb course to make beginning Cybersecurity Data Science
Amazon Verified review Amazon
Eduardo Bastos Jan 16, 2020
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Não vale a pena a aquisição deste produto. Mal formatado e sem contextualização. Dinheiro posto fora.
Amazon Verified review Amazon
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