A Comparative Study of Lightweight, Sparse Autoencoder-Based Classifiers for Edge Network Devices: An Efficiency Analysis of Feed-Forward and Deep Neural Networks

This study proposes a lightweight classification framework for anomaly traffic detection in edge computing environments. Thirteen packet- and flow-level features extracted from the CIC-IDS2017 dataset were compressed into 4-dimensional latent vectors using a Sparse Autoencoder (SAE). Two classifiers...

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Detalles Bibliográficos

Principais autores:	Mi Young Jo, Hyun Jung Kim
Formato:	Artigo
Idioma:	Inglês
Publicado:	MDPI AG 2025-10-01
Series:	Sensors
Assuntos:	edge computing sparse autoencoder lightweight classifier network anomaly detection feed-forward neural network deep neural network
Acceso en liña:	https://www.mdpi.com/1424-8220/25/20/6439
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