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
Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning
Our proposed scheme, called "Deep Packet," can handle both \emph{traffic characterization} in which the network traffic is categorized into major classes (\eg, FTP and P2P) and application identification in which end-user applications (\eg, BitTorrent and Skype) identification is desired.
Multitask Learning for Network Traffic Classification
We show that with a large amount of easily obtainable data samples for bandwidth and duration prediction tasks, and only a few data samples for the traffic classification task, one can achieve high accuracy.
Vehicle Position Estimation with Aerial Imagery from Unmanned Aerial Vehicles
fkthi/OpenTrafficMonitoringPlus
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A robust object detection is crucial for reliable results, hence the state-of-the-art deep neural network Mask-RCNN is applied for that purpose.
Deep Learning for Network Traffic Classification
We propose a classification technique using an ensemble of deep learning architectures on packet, payload, and inter-arrival time sequences.
A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic Classification
xaiseries/lexnet
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Traffic classification, i. e. the identification of the type of applications flowing in a network, is a strategic task for numerous activities (e. g., intrusion detection, routing).
ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification
In this paper, we propose a new traffic representation model called Encrypted Traffic Bidirectional Encoder Representations from Transformer (ET-BERT), which pre-trains deep contextualized datagram-level representation from large-scale unlabeled data.
Open-Source Framework for Encrypted Internet and Malicious Traffic Classification
Internet traffic classification plays a key role in network visibility, Quality of Services (QoS), intrusion detection, Quality of Experience (QoE) and traffic-trend analyses.
Segmented Learning for Class-of-Service Network Traffic Classification
The commonality of statistical features among the network flow segments motivates us to propose novel segmented learning that includes essential vector representation and a simple-segment method of classification.
MTC: A Multi-Task Model for Encrypted Network Traffic Classification Based on Transformer and 1D-CNN
yuchengml/MTC
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Intelligent Automation & Soft Computing 2023
Convolutional Neural Network (CNN) and Transformer are deep learning-based approaches for network traffic classification.
Network Traffic Classification based on Single Flow Time Series Analysis
Network traffic monitoring using IP flows is used to handle the current challenge of analyzing encrypted network communication.
