From machine learning to federated knowledge systems. Current challenges and future architectural directions for cybersecurity applications
Loading...
Authors
Author (Corporation)
Publication date
2025
Type of student thesis
Course of study
Collections
Type
04B - Conference paper
Editor (Corporation)
Supervisor
Parent work
Digitalization and Management Innovation IV. Proceedings of DMI 2025, Hong Kong, 7-9 July 2025
Special issue
DOI of the original publication
Series
Frontiers in Artificial Intelligence and Applications
Series number
412
Volume
Issue / Number
Pages / Duration
241-248
Patent number
Publisher / Publishing institution
IOS Press
Place of publication / Event location
Hong Kong
Edition
Version
Programming language
Assignee
Practice partner / Client
Abstract
Machine learning (ML) is foundational to cybersecurity today, underpinning critical applications such as intrusion detection systems. Despite their widespread adoption, however, established ML approaches are beginning to show critical limitations in handling evolving cyberthreats. In this paper, we (1.) conduct a data-driven analysis of ML for intrusion detection to understand and demonstrate current limitations, (2.) discuss the literature and contextualize the findings, and (3.) identify and outline architectural directions for advancing local ML to federated knowledge systems for cybersecurity applications. The analysis results indicate significant challenges within established systems, notably in defining normal behavior, high false alarm rates, and detection rates. These shortcomings highlight intrinsic constraints of localized ML approaches, as exemplified in intrusion detection and beyond. Toward addressing these issues, the properties of a federated knowledge system architecture can provide distributed data inputs rather than siloed sources, data sharing, and federated learning to derive widely distributed, structured knowledge bases.
Keywords
Subject (DDC)
Event
The 4th International Conference on Digitalization and Management Innovation (DMI2025)
Exhibition start date
Exhibition end date
Conference start date
07.07.2025
Conference end date
09.07.2025
Date of the last check
ISBN
978-1-64368-627-1
ISSN
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the abstract
Open access category
Gold
License
Citation
Härer, F., & Agostinis, N. (2025). From machine learning to federated knowledge systems. Current challenges and future architectural directions for cybersecurity applications. In A. Lau & A. J. Tallón-Ballesteros (Eds.), Digitalization and Management Innovation IV. Proceedings of DMI 2025, Hong Kong, 7-9 July 2025 (pp. 241–248). IOS Press. https://doi.org/10.3233/FAIA250724