ARMBoost+: Empowering stacking, ensemble, and boosting models for network intrusion detection with dynamic rule repository

As network security threats become increasingly complex, the need for efficient and effective network intrusion detection systems (NIDS) is more important than ever. Machine learning (ML) has emerged as a promising solution for NIDS due to its ability to analyze large volumes of network traffic data...

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Bibliographic Details
Published in:	Journal of network and computer applications Vol. 243; p. 104292
Main Authors:	Vivek, Vullikanti, Veeravalli, Bharadwaj
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.11.2025
Subjects:	Accuracy Associative rule mining Bloom filter Boosting Detection rate Dynamic rule repository building Ensemble False positives Feature repository Machine learning Network Intrusion Detection Security Stacking classifier Detection rate Dynamic rule repository building Stacking classifier Boosting Bloom filter Network Intrusion Detection Security Accuracy Machine learning Ensemble Feature repository False positives Associative rule mining
ISSN:	1084-8045
Online Access:	Get full text
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