Papers with Code 아카이브
Traffic Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 0
- 보존된 평가 기록
- 0
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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Efficient Traffic Classification using HW-NAS: Advanced Analysis and Optimization for Cybersecurity on Resource-Constrained Devices
When Simple Model Just Works: Is Network Traffic Classification in Crisis?
Respond to Change with Constancy: Instruction-tuning with LLM for Non-I.I.D. Network Traffic Classification
Intelligent Detection of Non-Essential IoT Traffic on the Home Gateway
FastFlow: Early Yet Robust Network Flow Classification using the Minimal Number of Time-Series Packets
Hierarchical Local-Global Feature Learning for Few-shot Malicious Traffic Detection
Large Language Models powered Malicious Traffic Detection: Architecture, Opportunities and Case Study
PacketCLIP: Multi-Modal Embedding of Network Traffic and Language for Cybersecurity Reasoning
Network Traffic Classification Using Machine Learning, Transformer, and Large Language Models
Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success
Revolutionizing Encrypted Traffic Classification with MH-Net: A Multi-View Heterogeneous Graph Model
NetFlowGen: Leveraging Generative Pre-training for Network Traffic Dynamics
VINEVI: A Virtualized Network Vision Architecture for Smart Monitoring of Heterogeneous Applications and Infrastructures
Improving the network traffic classification using the Packet Vision approach
MIETT: Multi-Instance Encrypted Traffic Transformer for Encrypted Traffic Classification
MERLOT: A Distilled LLM-based Mixture-of-Experts Framework for Scalable Encrypted Traffic Classification
Discern-XR: An Online Classifier for Metaverse Network Traffic
Group & Reweight: A Novel Cost-Sensitive Approach to Mitigating Class Imbalance in Network Traffic Classification
Packet Inspection Transformer: A Self-Supervised Journey to Unseen Malware Detection with Few Samples
AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression with Minimal Impact on Traffic Classification
Towards a graph-based foundation model for network traffic analysis
Time-Distributed Feature Learning for Internet of Things Network Traffic Classification
TrafficGPT: An LLM Approach for Open-Set Encrypted Traffic Classification
Efficiently and Effectively: A Two-stage Approach to Balance Plaintext and Encrypted Text for Traffic Classification
Towards a Transformer-Based Pre-trained Model for IoT Traffic Classification
Enhancing Encrypted Internet Traffic Classification Through Advanced Data Augmentation Techniques
Development of Multistage Machine Learning Classifier using Decision Trees and Boosting Algorithms over Darknet Network Traffic
Non-uniformity is All You Need: Efficient and Timely Encrypted Traffic Classification With ECHO
NetMamba: Efficient Network Traffic Classification via Pre-training Unidirectional Mamba
Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities
Generic Multi-modal Representation Learning for Network Traffic Analysis
FedAuxHMTL: Federated Auxiliary Hard-Parameter Sharing Multi-Task Learning for Network Edge Traffic Classification
CBR - Boosting Adaptive Classification By Retrieval of Encrypted Network Traffic with Out-of-distribution
One Train for Two Tasks: An Encrypted Traffic Classification Framework Using Supervised Contrastive Learning
Deep Learning Approaches for Network Traffic Classification in the Internet of Things (IoT): A Survey
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.