The proliferation of Internet of Things (IoT) devices has brought unprecedented convenience to our lives, but it has also opened the door to new security challenges. One of the most pressing threats in the IoT landscape is the proliferation of BotNets, which can compromise and control a multitude of devices for malicious purposes. In this paper, we propose a novel approach to address this issue: a Federated Machine Learning Solution for BotNet detection in IoT environments. Our method leverages the collective intelligence of distributed IoT devices while respecting privacy constraints, ensuring that sensitive data never leaves the device. We present a detailed methodology for federated model construction, including data collection, local model training, and secure aggregation. The resulting federated model offers improved accuracy and robustness in BotNet detection, as demonstrated through rigorous evaluation on the N-BaIoT dataset. Our findings underscore the effectiveness of this approach in enhancing IoT device security by detecting and mitigating BotNet threats while safeguarding data privacy. This paper contributes to the advancement of IoT security strategies and provides a framework for protecting IoT devices against evolving threats in a federated and privacy-preserving manner.

Thermal comfort prediction within sustainable built environments stands as a pivotal challenge intertwining human well-being and environmental sustainability. This paper presents a pioneering framework leveraging machine learning methodologies to advance predictive models for thermal comfort. Drawing upon a comprehensive dataset sourced from ASHRAE field studies and the RP-884 database, comprising 107,463 entries, our study unfolds a novel approach to enhancing thermal comfort predictions. The integration of diverse physiological parameters, environmental data, and occupant preferences forms the foundation of our machine learning-driven framework. Through meticulous analysis and model development, our approach not only refines predictive accuracy but also underscores adaptability across varying environmental contexts. The study contributes not only to the discourse on thermal comfort prediction but also emphasizes the crucial nexus between sustainable design, occupant well-being, and energy efficiency.