In certain situations, there is a considerable advantage to being able to detect malware based on its behavior. In particular, it is much more difficult for a malware to hide its intentions when it is being analyzed in a dynamic situation. For this reason, classifiers that operate on dynamic information can be much more accurate than their static counterparts. In this section, we provide a recipe for a dynamic malware classifier. The dataset we use is part of a VirusShare repository from android applications. The dynamic analysis was performed by Johannes Thon on several LG Nexus 5 devices with Android API 23, (over 4,000 malicious apps were dynamically analyzed on the LG Nexus 5 device farm (API 23), and over 4,300 benign apps were dynamically analyzed on the LG Nexus 5 device farm (API 23) by goorax, used under CC BY / unmodified from the...
Machine Learning for Cybersecurity Cookbook
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Machine Learning for Cybersecurity Cookbook
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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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Current Title:
Machine Learning for Cybersecurity Cookbook
Table of Contents (11 chapters)
Preface
Free Chapter
Machine Learning for Cybersecurity
Machine Learning-Based Malware Detection
Advanced Malware Detection
Machine Learning for Social Engineering
Penetration Testing Using Machine Learning
Automatic Intrusion Detection
Securing and Attacking Data with Machine Learning
Secure and Private AI
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