arXiv (OAI Expanded)
open access
TDDM-Melatt: A Decoupled Memory and Diffusion Framework for Generalizable Encrypted Traffic Classification
The widespread adoption of encrypted traffic poses severe challenges to current security situational awareness systems based on network traffic monitoring. In existing dataset-driven training and testing studies, limitations such as shortcut learning induced by spurious feature correlations and samp
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
- Transport traffic indicators#139976
- Optical network security management: requirements, architecture, and efficient machine learning models for detection of evolving threats [Invited]#328790
- When Correlations Mislead: Confounder-Aware Multi-View Urban Region Representation Learning#1109335
- A Priority-Aware Dual-Channel Feature Fusion Method for Urban Rail Service Traffic Classification#1116499
- Data-Driven Measures of High-Frequency Trading#821969
Record · ID 1069898
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