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Lightweight machine learning for privacy-preserving and secure networked medical devices: The SEPTON project use cases

Published: 10 August 2023 Publication History

Abstract

Cybersecurity incidents are among the greatest concerns of businesses, government agencies, and private citizens today. In the modern world, the protection of data and information assets has become nearly as important as maintaining the security of physical assets. This creates the need for increased security implementations, leading to improved user acceptance of such applications and, as a consequence, to large-scale adoption of these technologies and full exploitation of their advantages. In healthcare, networked medical devices (NMDs), either referring to hospital medical equipment or wearables, can be vulnerable to security breaches, potentially affecting the safety and effectiveness of each device. In this work, we present the specific areas of recent machine learning research applied to networked medical device security, through the objectives of the Horizon Europe SEPTON research project. State-of-the-art lightweight machine learning approaches are highlighted and the corresponding challenges of cybersecurity applications, ranging from implantable devices to inter-institution medical data exchange use cases, are showcased.

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Response to reviewers' comments about the PETRA 23 conference paper with ID 105 and paper acronym petra23

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  • (2025)Learning adversarially robust kernel ensembles with kernel average poolingExpert Systems with Applications10.1016/j.eswa.2024.126017266(126017)Online publication date: Mar-2025

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      Published In

      cover image ACM Other conferences
      PETRA '23: Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments
      July 2023
      797 pages
      ISBN:9798400700699
      DOI:10.1145/3594806
      This work is licensed under a Creative Commons Attribution International 4.0 License.

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 10 August 2023

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      Author Tags

      1. anomaly detection
      2. lightweight machine learning
      3. networked medical devices
      4. neural networks

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      • (2025)Learning adversarially robust kernel ensembles with kernel average poolingExpert Systems with Applications10.1016/j.eswa.2024.126017266(126017)Online publication date: Mar-2025

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