Multi-layer stacking ensemble learners for low footprint network intrusion detection
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
2198-6053
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
online resource.
- Erschienen in
-
Multi-layer stacking ensemble learners for low footprint network intrusion detection ; day:5 ; month:7 ; year:2022 ; pages:1-13
Complex & intelligent systems ; (5.7.2022), 1-13
- Urheber
-
Shafieian, Saeed
Zulkernine, Mohammad
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1007/s40747-022-00809-3
- URN
-
urn:nbn:de:101:1-2022082419550369385000
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:28 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Shafieian, Saeed
- Zulkernine, Mohammad
- SpringerLink (Online service)
Ähnliche Objekte (12)
Effective network intrusion detection using stacking-based ensemble approach
View selection in multi-view stacking: choosing the meta-learner
Ternary π–π stacking complexes by allosteric regulation in multilayer nanographenes
A novel stacking ensemble learner for predicting residual strength of corroded pipelines
Stacking, blending & reclaiming
Multi-layer intrusion detection system with ExtraTrees feature selection, extreme learning machine ensemble, and softmax aggregation
Water footprint and carbon footprint: Disparate relatives
Water footprint and carbon footprint - disparate relatives
Water footprint and carbon footprint - disparate relatives
Footprint reflections
Molecular Stacking on Graphene
Molecular Stacking on Graphene
Effective network intrusion detection using stacking-based ensemble approach
View selection in multi-view stacking: choosing the meta-learner
Ternary π–π stacking complexes by allosteric regulation in multilayer nanographenes
A novel stacking ensemble learner for predicting residual strength of corroded pipelines
Stacking, blending & reclaiming
Multi-layer intrusion detection system with ExtraTrees feature selection, extreme learning machine ensemble, and softmax aggregation
Water footprint and carbon footprint: Disparate relatives
Water footprint and carbon footprint - disparate relatives
Water footprint and carbon footprint - disparate relatives
Footprint reflections
Molecular Stacking on Graphene
Molecular Stacking on Graphene
Effective network intrusion detection using stacking-based ensemble approach
View selection in multi-view stacking: choosing the meta-learner
Ternary π–π stacking complexes by allosteric regulation in multilayer nanographenes
A novel stacking ensemble learner for predicting residual strength of corroded pipelines
Stacking, blending & reclaiming
Multi-layer intrusion detection system with ExtraTrees feature selection, extreme learning machine ensemble, and softmax aggregation
Water footprint and carbon footprint: Disparate relatives
Water footprint and carbon footprint - disparate relatives
Water footprint and carbon footprint - disparate relatives
Footprint reflections
Molecular Stacking on Graphene