Angielski
Advanced Machine Learning for Cyber-Attack Detection in IoT Networks offers a comprehensive overview of cybersecurity threats in future IoT systems. The book analyzes diverse machine learning techniques, including supervised, unsupervised, reinforcement, and deep learning, and their applications in detecting and preventing cyber-attacks in future IoT systems. Chapters will investigate the key challenges and vulnerabilities found in IoT security, how to handle challenges in data collection and pre-processing specific to IoT environments, as well as what metrics to consider for evaluating the performance of machine learning models. Chapters will look at the training, validation, and evaluation of supervised learning models and present case studies and examples demonstrating the application of supervised learning in IoT security. The target audience for this book includes, but is not limited to, graduate students, researchers, and practicing engineers.
