Springer Nature OA
open access
CC BY-NC-ND 4.0
Hybrid dynamic feature convolution with transformer fusion model for the detection of intrusion during manufacturing process
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- A high-performance intrusion detection model for IIoT networks using BBO-based feature selection and deep fusion#237356
- A hybrid intrusion detection method based on multi-convolutional neural networks fusion and grey wolf optimizer#203584
- NFAD: Nuisance-Filtered Anomaly Detection Under Distribution Shift#793334
- NFAD: Nuisance-Filtered Anomaly Detection Under Distribution Shift#789330
- A stacking ensemble of deep learning models for IoT intrusion detection#200935
Record · ID 443659
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive