TL;DR
The progressive design framing yields an actionable architecture-selection rule for security operations centres: match architectural complexity to dataset heterogeneity rather than maximising it by default.
An Enhanced Multi-Model Ensemble Network Intrusion Detection System (EME-NIDS), a deep meta-learning system that combines five different heterogeneous learning paradigms, including Convolutional Neural Networks, Dense Neural Networks, Transformers, XGBoost, and Random Forests is introduced.
The principal contribution of this work is architectural and diagnostic rather than a performance improvement: it documents that combining feature-wise attention with out-of-fold stacked generalization does not, in this setting, outperform a plain multi-layer perceptron, while incurring the highest memory footprint of...
Network Intrusion Detection Systems (NIDS) based on machine learning must evolve after deployment to promptly detect emerging threats from only a few labeled samples, without catastrophically forgetting known attacks or degrading under concept drift. Existing methods address these issues in isolation, lacking a unified...
ManFormer is designed and implements, a hybrid Transformer–Mamba classification architecture that integrates Transformer and Mamba for high-dimensional imbalanced classification tasks, and achieves good overall performance in high-dimensional imbalanced multi-class intrusion detection tasks.
A Transformer-based network intrusion detection system (t-NIDS) that integrates a Transformer encoder with contrastive learning to learn discriminative and robust feature representations for network traffic classification is proposed, suitable for practical deployment in real-world network intrusion detection environme...
Across multilayer perceptron (MLP), one-dimensional convolutional neural network, and Feature Tokenizer + Transformer, MaskAug improves corruption-averaged Macro-F1 and delivers architecture-agnostic, cross-dataset-consistent robustness at zero inference-time cost.
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