Traffic Classification
29 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
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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
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.
Practical and Configurable Network Traffic Classification Using Probabilistic Machine Learning
Network traffic classification that is widely applicable and highly accurate is valuable for many network security and management tasks.
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).
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.
When a RF Beats a CNN and GRU, Together -- A Comparison of Deep Learning and Classical Machine Learning Approaches for Encrypted Malware Traffic Classification
Internet traffic classification is widely used to facilitate network management.
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.