Personalization is an important factor to increase the user experience (UX) and effectiveness of mHealth solutions. In this paper, we present an innovative approach to the personalization of mHealth apps. A profiling function has been developed based on the physical and psychological characteristics of users, with the final aim to cluster them acting as a guideline to the design and implementation of new functionalities to improve the overall acceptance degree of the app. A preliminary analysis case study has been proposed to evaluate the impact on user experience according to the state of the art to draw useful lessons for future works.
The Internet of Things (IoT) is growing rapidly and so the need of ensuring protection against cybersecurity attacks to IoT devices. In this scenario, Intrusion Detection Systems (IDSs) play a crucial role and data-driven IDSs based on machine learning (ML) have recently attracted more and more interest by the research community. While conventional ML-based IDSs are based on a centralized architecture where IoT devices share their data with a central server for model training, we propose a novel approach that is based on federated learning (FL). However, conventional FL is ine ective in the considered scenario, due to the high statistical heterogeneity of data collected by IoT devices. To overcome this limitation, we propose a three-tier FL-based architecture where IoT devices are clustered together based on their statistical properties. Clustering decisions are taken by means of a novel entropy-based strategy, which helps improve model training performance. We tested our solution on the CIC-ToN-IoT dataset: our clustering strategy increases intrusion detection performance with respect to a conventional FL approach up to +17% in terms of F1-score, along with a significant reduction of the number of training rounds.
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