Machine Learning for Cybersecurity Cookbook: Over 80 recipes on how to implement machine learning algorithms for building security systems using Python by Emmanuel Tsukerman
Requirements: .ePUB reader, 59 MB
Overview: Learn how to apply modern AI to create powerful cybersecurity solutions for malware, pentesting, social engineering, data privacy, and intrusion detection
Key Features, 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
Book Description, Organizations today face a major threat in terms of cybersecurity, right from malicious URLs to credential reuse, and having robust security systems can make all the difference. With this book, you’ll learn to use Python libraries such as TensorFlow and scikit-learn to implement the latest 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 machine learning 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 artificial intelligence (AI) to protect the privacy rights of consumers using your ML models.
Genre: Non-Fiction > Tech & Devices
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