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

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...

Full description

Saved in:
Bibliographic Details
Main Authors: Anubhav Kumar, Rajamani Radhakrishnan, Mani Sumithra, Prabu Kaliyaperumal, Balamurugan Balusamy, Francesco Benedetto
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Future Internet
Subjects:
intrusion detection system
autoencoder
extreme learning machine
cloud continuum
edge–fog–cloud orchestration
scalable iot security
Online Access:https://www.mdpi.com/1999-5903/17/5/221
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items