on 26-10-2026
The purpose of the International Conference on Computer-Human Interaction Research and Applications (CHIRA) is to bring together professionals, academics, and students who are interested in the advancement of research and practical applications in its field of interest, covering different aspects of Computer-Human Interaction, including Human Factors and Information Systems, Interactive Devices, Interaction Design, and Adaptive and Intelligent Systems.
The proceedings will be published by Springer in a CCIS Series book.
Conference Areas
Conference chair
Pietro Cipresso, Department of Psychology, University of Turin, Italy
Program Co-Chairs
Hugo Plácido da Silva, IT- Instituto de Telecomunicações, Portugal
Josef F. Krems, Chemnitz University of Technology, Cognitive and Engineering Psychology, Germany
Important dates
Position Papers /Regular Papers
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More Information..on 13-07-2026
Instituto de Telecomunicações (IT) will host the seminar "A Hierarchical Framework for Anomaly Detection and Attack Classification on the Internet of Medical Things (IoMT)" on July 13, 02:30 to 04:00, at the Faculty of Engineering, DEM - Instituto de Telecomunicações, Universidade da Beira Interior (room 08.01). The invited speaker is Yeritza Gómez, a Master's student at the National Institute of Astrophysics, Optics and Electronics (INAOE), Mexico.
The seminar will explore innovative approaches to strengthening cybersecurity in the rapidly evolving Internet of Medical Things (IoMT), highlighting a hierarchical framework that combines edge-based anomaly detection with cloud-based attack classification to enhance the protection of healthcare systems.
Abstract:
The widespread adoption of the Internet of Medical Things (IoMT) has transformed healthcare by enabling real-time data exchange and seamless connectivity between medical devices. However, this increased interconnectivity has also expanded the attack surface, making healthcare infrastructures more vulnerable to cyber threats.
Traditional intrusion detection systems (IDS) are generally centralized and computationally demanding, limiting their applicability in resource-constrained IoMT environments. This presentation introduces a hierarchical and modular detection framework that distributes anomaly detection to edge devices while delegating attack classification to cloud-based machine learning models. By leveraging edge interactions, the proposed architecture enables early threat detection without compromising network performance, offering an efficient and scalable cybersecurity solution for IoMT ecosystems.
Bio:
Yeritza Gómez is a Biomedical Engineer who graduated from the National Polytechnic Institute (IPN) in Mexico, where she conducted research on physiological signal analysis during N95 mask use under different exercise conditions. She has collaborated on several neuroengineering instrumentation and physiological signal processing projects with universities across Mexico. Professionally, she has worked in medical equipment maintenance and procurement, as well as in the education sector, teaching students at various educational levels in both public and private institutions. She is currently pursuing a Master's degree in Computational Sciences at the National Institute of Astrophysics, Optics and Electronics (INAOE), where she is a member of the Cybersecurity Laboratory. Her research focuses on applying machine learning techniques to attack detection and classification in Medical Internet of Things (IoMT) environments.
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