\u003ci\u003eAdvanced Machine Learning for Cyber-Attack Detection in IoT Networks\u003c/i\u003e analyzes diverse machine learning techniques, including supervised, unsupervised, reinforcement, and deep learning, along with their applications in detecting and preventing cyberattacks in future IoT systems. Chapters 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. Other sections look at the training, validation, and evaluation of supervised learning models and present case studies and examples that demonstrate the application of supervised learning in IoT security.\u003cul\u003e\u003cli\u003ePresents a comprehensive overview of research on IoT security threats and potential attacks\u003c/li\u003e\u003cli\u003eInvestigates machine learning techniques, their mathematical foundations, and their application in cybersecurity\u003c/li\u003e\u003cli\u003ePresents metrics for evaluating the performance of machine learning models as well as benchmark datasets and evaluation frameworks for assessing IoT systems\u003c/li\u003e\u003c/ul\u003e
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