Librería Portfolio

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.