Intelligent and Trustworthy IoT Systems: Challenges and Solutions in Forecasting, Explainability, and Security

This talk explores the challenges and emerging solutions associated with developing intelligent and trustworthy IoT systems. We begin by examining time series forecasting in resource-constrained IoT environments, highlighting techniques for accurate predictions of time-series data. Building on this, we discuss the role of explainable AI (XAI) in enhancing the transparency and trustworthiness of machine learning models within IoT frameworks.

Finally, we address critical security issues, focusing on recent approaches for detecting and mitigating botnet attacks. By integrating insights from research and practical applications, this talk aims to provide an overview of the current landscape and future directions in creating secure, efficient, and transparent IoT systems.

Coordinators

Ruben van Beesten, Olga Kuryatnikova, Ece Karakoyun