Enhancing Operational Security of Human-to-Machine Applications through Concept Drift Detection
We propose a novel concept drift detection framework to detect and mitigate malicious traffic for human-to-machine applications in fiber-wireless access networks, reducing uplink latency by up to 98% and enhancing its operational security.
Authors
- Carlos Natalino (ORCID: https://orcid.org/0000-0001-7501-5547)
- Paolo Monti (ORCID: https://orcid.org/0000-0002-5636-9910)
- Elaine Wong (ORCID: https://orcid.org/0000-0002-2561-3482)
- Lena Wosinska (ORCID: https://orcid.org/0000-0001-6704-6554)
- Sourav Mondal (ORCID: https://orcid.org/0000-0002-3626-3254)
- Yuxiao Wang (ORCID: https://orcid.org/0009-0001-7359-5231)
- Xiangyu Yu (ORCID: https://orcid.org/0000-0003-2293-8430)
Institutions
- The University of Melbourne (AU)
- Chalmers University of Technology (SE)
- Victoria University (AU)
Publication Details
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1364/ofc.2025.w3j.5
- Citations
- 1
- Primary Topic
- Data Stream Mining Techniques
- Type
- article
- Field-Weighted Citation Impact
- 2.48