Traffic Classification
17 papers with code • 0 benchmarks • 1 datasets
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.
Benchmarks
These leaderboards are used to track progress in Traffic Classification
LEAF Benchmark
Most implemented papers
TFE-GNN: A Temporal Fusion Encoder Using Graph Neural Networks for Fine-grained Encrypted Traffic Classification
Encrypted traffic classification is receiving widespread attention from researchers and industrial companies.
AutoML4ETC: Automated Neural Architecture Search for Real-World Encrypted Traffic Classification
However, in production use, it has been shown that a DL classifier's performance inevitably decays over time.
Privacy-preserving Few-shot Traffic Detection against Advanced Persistent Threats via Federated Meta Learning
spttt/Per-FedAvg-APT
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IEEE Transactions on Network Science and Engineering 2023
Extensive experiments based on multiple benchmark datasets like CICIDS2017 and DAPT 2020 prove the superiority of proposed PFTD.
Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input Representation
Over the last years we witnessed a renewed interest toward Traffic Classification (TC) captivated by the rise of Deep Learning (DL).
NetTiSA: Extended IP Flow with Time-series Features for Universal Bandwidth-constrained High-speed Network Traffic Classification
Network traffic monitoring based on IP Flows is a standard monitoring approach that can be deployed to various network infrastructures, even the large IPS-based networks connecting millions of people.
DataZoo: Streamlining Traffic Classification Experiments
The DataZoo toolset simplifies the creation of realistic evaluation scenarios, making it easier to cross-compare classification methods and reproduce results.
One Train for Two Tasks: An Encrypted Traffic Classification Framework Using Supervised Contrastive Learning
In particular, we utilize supervised contrastive learning to enhance the packet-level and flow-level representations and perform graph data augmentation on the byte-level traffic graph so that the fine-grained semantic-invariant characteristics between bytes can be captured through contrastive learning.
