From machine learning to federated knowledge systems. Current challenges and future architectural directions for cybersecurity applications
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Autor:innen
Autor:in (Körperschaft)
Publikationsdatum
2025
Typ der Arbeit
Studiengang
Typ
04B - Beitrag Konferenzschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Digitalization and Management Innovation IV. Proceedings of DMI 2025, Hong Kong, 7-9 July 2025
Themenheft
DOI der Originalpublikation
Reihe / Serie
Frontiers in Artificial Intelligence and Applications
Reihennummer
412
Jahrgang / Band
Ausgabe / Nummer
Seiten / Dauer
241-248
Patentnummer
Verlag / Herausgebende Institution
IOS Press
Verlagsort / Veranstaltungsort
Hong Kong
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
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.
Schlagwörter
Fachgebiet (DDC)
Veranstaltung
The 4th International Conference on Digitalization and Management Innovation (DMI2025)
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
07.07.2025
Enddatum der Konferenz
09.07.2025
Datum der letzten Prüfung
ISBN
978-1-64368-627-1
ISSN
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
Peer-Review des Abstracts
Open Access-Status
Gold
Lizenz
Zitation
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