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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 1–50 of 110 papers

TitleStatusHype
Revolutionizing Encrypted Traffic Classification with MH-Net: A Multi-View Heterogeneous Graph ModelCode2
TrafficGPT: An LLM Approach for Open-Set Encrypted Traffic ClassificationCode2
NetMamba: Efficient Network Traffic Classification via Pre-training Unidirectional MambaCode2
One Train for Two Tasks: An Encrypted Traffic Classification Framework Using Supervised Contrastive LearningCode2
ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic ClassificationCode2
Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning SuccessCode1
MIETT: Multi-Instance Encrypted Traffic Transformer for Encrypted Traffic ClassificationCode1
DataZoo: Streamlining Traffic Classification ExperimentsCode1
netFound: Foundation Model for Network SecurityCode1
NetDiffus: Network Traffic Generation by Diffusion Models through Time-Series ImagingCode1
Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input RepresentationCode1
Privacy-preserving Few-shot Traffic Detection against Advanced Persistent Threats via Federated Meta LearningCode1
AutoML4ETC: Automated Neural Architecture Search for Real-World Encrypted Traffic ClassificationCode1
TFE-GNN: A Temporal Fusion Encoder Using Graph Neural Networks for Fine-grained Encrypted Traffic ClassificationCode1
Network Traffic Classification based on Single Flow Time Series AnalysisCode1
MTC: A Multi-Task Model for Encrypted Network Traffic Classification Based on Transformer and 1D-CNNCode1
NetGPT: Generative Pretrained Transformer for Network TrafficCode1
Open-Source Framework for Encrypted Internet and Malicious Traffic ClassificationCode1
A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic ClassificationCode1
Deep Learning for Network Traffic ClassificationCode1
Vehicle Position Estimation with Aerial Imagery from Unmanned Aerial VehiclesCode1
Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep LearningCode1
Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers—0
Efficient Traffic Classification using HW-NAS: Advanced Analysis and Optimization for Cybersecurity on Resource-Constrained Devices—0
When Simple Model Just Works: Is Network Traffic Classification in Crisis?—0
Respond to Change with Constancy: Instruction-tuning with LLM for Non-I.I.D. Network Traffic Classification—0
Intelligent Detection of Non-Essential IoT Traffic on the Home Gateway—0
FastFlow: Early Yet Robust Network Flow Classification using the Minimal Number of Time-Series Packets—0
Hierarchical Local-Global Feature Learning for Few-shot Malicious Traffic Detection—0
Large Language Models powered Malicious Traffic Detection: Architecture, Opportunities and Case Study—0
PacketCLIP: Multi-Modal Embedding of Network Traffic and Language for Cybersecurity Reasoning—0
Network Traffic Classification Using Machine Learning, Transformer, and Large Language Models—0
NetFlowGen: Leveraging Generative Pre-training for Network Traffic Dynamics—0
Improving the network traffic classification using the Packet Vision approach—0
VINEVI: A Virtualized Network Vision Architecture for Smart Monitoring of Heterogeneous Applications and Infrastructures—0
MERLOT: A Distilled LLM-based Mixture-of-Experts Framework for Scalable Encrypted Traffic Classification—0
Discern-XR: An Online Classifier for Metaverse Network Traffic—0
Group & Reweight: A Novel Cost-Sensitive Approach to Mitigating Class Imbalance in Network Traffic Classification—0
Packet Inspection Transformer: A Self-Supervised Journey to Unseen Malware Detection with Few Samples—0
AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression with Minimal Impact on Traffic ClassificationCode0
Towards a graph-based foundation model for network traffic analysis—0
Time-Distributed Feature Learning for Internet of Things Network Traffic Classification—0
Efficiently and Effectively: A Two-stage Approach to Balance Plaintext and Encrypted Text for Traffic Classification—0
Towards a Transformer-Based Pre-trained Model for IoT Traffic ClassificationCode0
Enhancing Encrypted Internet Traffic Classification Through Advanced Data Augmentation TechniquesCode0
Development of Multistage Machine Learning Classifier using Decision Trees and Boosting Algorithms over Darknet Network Traffic—0
Non-uniformity is All You Need: Efficient and Timely Encrypted Traffic Classification With ECHO—0
Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities—0
Generic Multi-modal Representation Learning for Network Traffic Analysis—0
FedAuxHMTL: Federated Auxiliary Hard-Parameter Sharing Multi-Task Learning for Network Edge Traffic Classification—0
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