SVM-RFE with optimization-based feature selection and parallel convolutional stacked autoencoder for detecting minority-class sample attacks

In intrusion detection systems, a critical imbalance in the attack and normal sample quantities leads to low detection accuracy for minority-class attacks. This study introduces an innovative method, namely Parallel Convolutional Stacked Autoencoder (PConVSA-Net), for minority-class attack detection...

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Veröffentlicht in:	Knowledge and information systems Jg. 67; H. 12; S. 11727 - 11761
Hauptverfasser:	Sasikumar, A. N., Suresh, S.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.12.2025 Springer Nature B.V
Schlagworte:	Accuracy Algorithms Artificial neural networks Computer Science Data augmentation Data Mining and Knowledge Discovery Database Management Datasets False alarms Feature selection Information Storage and Retrieval Information Systems and Communication Service Information Systems Applications (incl.Internet) Intrusion detection systems IT in Business Machine learning Neural networks Optimization Support vector machines Taylor pufferfish optimization algorithm Support vector machine with recursive feature elimination Synthetic minority oversampling technique Deep stacked autoencoder Parallel convolutional neural network
ISSN:	0219-1377, 0219-3116
Online-Zugang:	Volltext
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