Traffic Classification

3 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.

Source: Classification of Traffic Using Neural Networks by Rejecting: a Novel Approach in Classifying VPN Traffic

Greatest papers with code

Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning

YuriBogdanov/DeepPacket 8 Sep 2017

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.

Classification General Classification +1

Multitask Learning for Network Traffic Classification

shrezaei/MultitaskTrafficClassification 12 Jun 2019

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.

Classification General Classification +3

Vehicle Position Estimation with Aerial Imagery from Unmanned Aerial Vehicles

fkthi/OpenTrafficMonitoringPlus 17 Apr 2020

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.

drone-based object tracking Robust Object Detection +1