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Traffic Classification

Traffic Classification is a task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Classification can be used for several purposes including policy enforcement and control or QoS management.

Source: Classification of Traffic Using Neural Networks by Rejecting: a Novel Approach in Classifying VPN Traffic

Papers

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Showing 1–25 of 110 papers

TitleStatusHype
One Train for Two Tasks: An Encrypted Traffic Classification Framework Using Supervised Contrastive LearningCode2
NetMamba: Efficient Network Traffic Classification via Pre-training Unidirectional MambaCode2
TrafficGPT: An LLM Approach for Open-Set Encrypted Traffic ClassificationCode2
Revolutionizing Encrypted Traffic Classification with MH-Net: A Multi-View Heterogeneous Graph ModelCode2
ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic ClassificationCode2
Network Traffic Classification based on Single Flow Time Series AnalysisCode1
DataZoo: Streamlining Traffic Classification ExperimentsCode1
Open-Source Framework for Encrypted Internet and Malicious Traffic ClassificationCode1
netFound: Foundation Model for Network SecurityCode1
MTC: A Multi-Task Model for Encrypted Network Traffic Classification Based on Transformer and 1D-CNNCode1
NetGPT: Generative Pretrained Transformer for Network TrafficCode1
MIETT: Multi-Instance Encrypted Traffic Transformer for Encrypted Traffic ClassificationCode1
Privacy-preserving Few-shot Traffic Detection against Advanced Persistent Threats via Federated Meta LearningCode1
Vehicle Position Estimation with Aerial Imagery from Unmanned Aerial VehiclesCode1
Deep Learning for Network Traffic ClassificationCode1
AutoML4ETC: Automated Neural Architecture Search for Real-World Encrypted Traffic ClassificationCode1
Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep LearningCode1
TFE-GNN: A Temporal Fusion Encoder Using Graph Neural Networks for Fine-grained Encrypted Traffic ClassificationCode1
Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning SuccessCode1
Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input RepresentationCode1
A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic ClassificationCode1
NetDiffus: Network Traffic Generation by Diffusion Models through Time-Series ImagingCode1
AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression with Minimal Impact on Traffic ClassificationCode0
Practical and Configurable Network Traffic Classification Using Probabilistic Machine LearningCode0
NetTiSA: Extended IP Flow with Time-series Features for Universal Bandwidth-constrained High-speed Network Traffic ClassificationCode0
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