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Robust IoT Security: Intrusion Detection Leveraging Contributions from Multiple Systems

Reference number
Coordinator Uppsala universitet - Uppsala universitet Inst f informationsteknologi
Funding from Vinnova SEK 4 613 723
Project duration November 2023 - November 2025
Status Ongoing
Venture Advanced digitalization - Enabling technologies
Call Cyber security for industrial advanced digitalization 2023

Purpose and goal

Intrusion Detection Systems are critical components of an effective Internet of Things (IoT) cybersecurity defense strategy. Effective solutions based on machine learning (ML) relies on the availability of data about earlier attacks. The objective of the project is to explore and provide privacy-preserving and robust techniques to build strong IDS with contributions from multiple actors and systems. Specifically, the project focuses on methods for mitigating the expected data heterogeneity in federated learning among a set of IoT network providers.

Expected effects and result

The expected outcome of the project are novel methods based on federated learning (FL) specifically tailored for heterogeneous data from different actors and systems, and new platform enablers implemented in an open-source FL system, with future deployments across Swedish industry.

Planned approach and implementation

The project is a collaboration between Uppsala University (information technology) and Scaleout Systems AB. Selected results from research on knowledge sharing under data heterogeneity and model robustness will be implemented in Scaleout´s open-source federated learning platform, and thereby be made accessible for researchers and industry.

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 20 November 2023

Reference number 2023-02982

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