Traffic Classification

7 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

Most implemented papers

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

mhwong2007/Deep-Packet 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.

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.

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.

Deep Learning for Network Traffic Classification

niloofarbayat/NetworkClassification 2 Jun 2021

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 11 Feb 2022

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

linwhitehat/et-bert 13 Feb 2022

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.

Segmented Learning for Class-of-Service Network Traffic Classification

yoga-suhas-km/s2mc-for-cos 3 Aug 2022

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.