A Scalable Hybrid Autoencoder–Extreme Learning Machine Framework for Adaptive Intrusion Detection in High-Dimensional Networks

The rapid expansion of network environments has introduced significant cybersecurity challenges, particularly in handling high-dimensional traffic and detecting sophisticated threats. This study presents a novel, scalable Hybrid Autoencoder–Extreme Learning Machine (AE–ELM) framework for Intrusion D...

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Vydané v:	Future internet Ročník 17; číslo 5; s. 221
Hlavní autori:	Kumar, Anubhav, Radhakrishnan, Rajamani, Sumithra, Mani, Kaliyaperumal, Prabu, Balusamy, Balamurugan, Benedetto, Francesco
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 01.05.2025
Predmet:	Accuracy autoencoder Classification cloud continuum Cybersecurity Cyberterrorism Datasets Decision trees Detectors edge–fog–cloud orchestration Efficiency extreme learning machine Feature selection intrusion detection system Intrusion detection systems Machine learning Optimization Real time scalable iot security Support vector machines
ISSN:	1999-5903, 1999-5903
On-line prístup:	Získať plný text
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