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
Latest papers with no code
FedEdge AI-TC: A Semi-supervised Traffic Classification Method based on Trusted Federated Deep Learning for Mobile Edge Computing
The framework enhances user privacy and model credibility, offering a comprehensive solution for dependable and transparent Network TC in 5G CPE, thus enhancing service quality and security.
Real-time Traffic Classification for 5G NSA Encrypted Data Flows With Physical Channel Records
In this study, we examine the real-time encrypted 5G Non-Standalone (NSA) application-level traffic classification using physical channel records.
Many or Few Samples? Comparing Transfer, Contrastive and Meta-Learning in Encrypted Traffic Classification
The popularity of Deep Learning (DL), coupled with network traffic visibility reduction due to the increased adoption of HTTPS, QUIC and DNS-SEC, re-ignited interest towards Traffic Classification (TC).
NetGPT: Generative Pretrained Transformer for Network Traffic
Pretrained models for network traffic can utilize large-scale raw data to learn the essential characteristics of network traffic, and generate distinguishable results for input traffic without considering specific downstream tasks.
On the Local Cache Update Rules in Streaming Federated Learning
In this study, we address the emerging field of Streaming Federated Learning (SFL) and propose local cache update rules to manage dynamic data distributions and limited cache capacity.
Generative Adversarial Classification Network with Application to Network Traffic Classification
Large datasets in machine learning often contain missing data, which necessitates the imputation of missing data values.
OMINACS: Online ML-Based IoT Network Attack Detection and Classification System
Several Machine Learning (ML) methodologies have been proposed to improve security in Internet Of Things (IoT) networks and reduce the damage caused by the action of malicious agents.
Multi-view Multi-label Anomaly Network Traffic Classification based on MLP-Mixer Neural Network
In this paper, we propose an MLP-Mixer based multi-view multi-label neural network for network traffic classification.
Active Learning Framework to Automate NetworkTraffic Classification
The paper presents a novel ActiveLearning Framework (ALF) to address this topic.
To Store or Not? Online Data Selection for Federated Learning with Limited Storage
We first define a new data valuation metric for data evaluation and selection in FL with theoretical guarantees for speeding up model convergence and enhancing final model accuracy, simultaneously.
