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.
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Latest papers
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.
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.
A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic Classification
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).
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.
Vehicle Position Estimation with Aerial Imagery from Unmanned Aerial Vehicles
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.
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.
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.