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.
Review Written by Constantin Florea
Frequently Bought Together
If you love this, you might also like...
Mac & PC
25 Pitching Essentials
If you are like many people, even the thought of delivering a speech in front of an audience will get your palms sweating.
- Your discount coupon code will be applied to your purchase when you click the 'Buy Now' button.
- BitsDuJour downloads use a discount coupon code that comes direct from the software vendor, so you'll always get the latest version of the software app sold under the same terms as a regular sale, just at a great promotional price.
- Prices do not necessarily include taxes, which will vary by country.
