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.
Table of Contents (11 chapters)

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