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
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Zusammenfassung: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. Initially, the input data are extracted from the dataset and undergo a normalization process, where log transformation is used to modify the data into a standardized form. Further, the normalized data are then passed to the feature selection module. Here, a Support Vector Machine with Recursive Feature Elimination (SVM-RFE) is employed to select the features. The weight parameters of SVM are adjusted by the introduced Taylor Pufferfish Optimization Algorithm, which is established by compounding the Taylor-series concept into the Pufferfish optimization algorithm. Later, the Synthetic Minority Oversampling Technique is utilized to perform data augmentation. Lastly, the minority-class attack detection is done by the established PConvSA-Net, which is developed by merging the Parallel Convolutional Neural Network (Parallel-CNN) and Deep Stacked Autoencoder. For dataset 1, the introduced PconvSA-Net attained high accuracy and true-positive rate of 91.876% and 94.326%, a low false-positive rate of 9.786%, precision of 90.878%, and F1-score of 92.570%.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0219-1377
0219-3116
DOI:10.1007/s10115-025-02574-4