Book Image

Machine Learning for Cybersecurity Cookbook

By : Emmanuel Tsukerman
Book Image

Machine Learning for Cybersecurity Cookbook

By: Emmanuel Tsukerman

Overview of this book

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.

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Table of Contents (11 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Sections
Get in touch
Free Chapter
1
Machine Learning for Cybersecurity
Machine Learning for Cybersecurity
Technical requirements
Train-test-splitting your data
Standardizing your data
Summarizing large data using principal component analysis
Generating text using Markov chains
Performing clustering using scikit-learn
Training an XGBoost classifier
Analyzing time series using statsmodels
Anomaly detection with Isolation Forest
Natural language processing using a hashing vectorizer and tf-idf with scikit-learn
Hyperparameter tuning with scikit-optimize
2
Machine Learning-Based Malware Detection
Machine Learning-Based Malware Detection
Technical requirements
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-grams
Selecting the best N-grams
Building a static malware detector
Tackling class imbalance
Handling type I and type II errors
3
Advanced Malware Detection
Advanced Malware Detection
Technical requirements
Detecting obfuscated JavaScript
Featurizing PDF files
Extracting N-grams quickly using the hash-gram algorithm
Building a dynamic malware classifier
MalConv – end-to-end deep learning for malicious PE detection
Tackling packed malware
MalGAN – creating evasive malware
Tracking malware drift
4
Machine Learning for Social Engineering
Machine Learning for Social Engineering
Technical requirements
Twitter spear phishing bot
Voice impersonation
Speech recognition for OSINT
Facial recognition
Deepfake
Deepfake recognition
Lie detection using machine learning
Personality analysis
Social Mapper
Fake review generator
Fake news
5
Penetration Testing Using Machine Learning
Penetration Testing Using Machine Learning
Technical requirements
CAPTCHA breaker
Neural network-assisted fuzzing
DeepExploit
Web server vulnerability scanner using machine learning (GyoiThon)
Deanonymizing Tor using machine learning
IoT device type identification using machine learning
Keystroke dynamics
Malicious URL detector
Deep-pwning
Deep learning-based system for the automatic detection of software vulnerabilities
6
Automatic Intrusion Detection
Automatic Intrusion Detection
Technical requirements
Spam filtering using machine learning
Phishing URL detection
Capturing network traffic
Network behavior anomaly detection
Botnet traffic detection
Insider threat detection
Detecting DDoS
Credit card fraud detection
Counterfeit bank note detection
Ad blocking using machine learning
Wireless indoor localization
7
Securing and Attacking Data with Machine Learning
Securing and Attacking Data with Machine Learning
Technical requirements
Assessing password security using ML
Deep learning for password cracking
Deep steganography
ML-based steganalysis
ML attacks on PUFs
Encryption using deep learning
HIPAA data breaches – data exploration and visualization
8
Secure and Private AI
Secure and Private AI
Technical requirements
Federated learning
Encrypted computation
Private deep learning prediction
Testing the adversarial robustness of neural networks
Differential privacy using TensorFlow Privacy
9
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think
Appendix
Appendix
Setting up a virtual lab environment
Using Python virtual environments

To get the most out of this book

You will need a basic knowledge of Python and cybersecurity.

Download the example code files

You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packtpub.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

  1. Log in or register at www.packt.com.
  2. Select the Support tab.
  3. Click on Code Downloads.
  4. Enter the name of the book in the Search box and follow the onscreen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

  • WinRAR/7-Zip for Windows
  • Zipeg/iZip/UnRarX for Mac
  • 7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Machine-Learning-for-Cybersecurity-Cookbook. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "Append the labels to X_outliers."

A block of code is set as follows:

from sklearn.model_selection import train_test_split
import pandas as pd

Any command-line input or output is written as follows:

pip install sklearn pandas

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "The most basic approach to hyperparameter tuning is called a grid search."

Warnings or important notes appear like this.
Tips and tricks appear like this.
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