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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Vydáno v:	Journal of network and computer applications Ročník 243; s. 104292
Hlavní autoři:	Vivek, Vullikanti, Veeravalli, Bharadwaj
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.11.2025
Témata:	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
On-line přístup:	Získat plný text
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