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
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.
Traffic Analytics Development Kits (TADK): Enable Real-Time AI Inference in Networking Apps
Sophisticated traffic analytics, such as the encrypted traffic analytics and unknown malware detection, emphasizes the need for advanced methods to analyze the network traffic.
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.
Darknet Traffic Classification and Adversarial Attacks
Previous research has employed machine learning and deep learning techniques to automate the detection of darknet traffic in an attempt to block these criminal activities.
Network Traffic Anomaly Detection Method Based on Multi scale Residual Feature
To address the problem that traditional network traffic anomaly detection algorithms do not suffi-ciently mine potential features in long time domain, an anomaly detection method based on mul-ti-scale residual features of network traffic is proposed.
Federated Semi-Supervised Classification of Multimedia Flows for 3D Networks
The federated gateways of 3D network help to enhance the global knowledge of network traffic to improve the accuracy of anomaly and intrusion detection and service identification of a new traffic flow.
Deep Learning for Encrypted Traffic Classification and Unknown Data Detection
Despite the widespread use of encryption techniques to provide confidentiality over Internet communications, mobile device users are still susceptible to privacy and security risks.
A General Approach for Traffic Classification in Wireless Networks Using Deep Learning
To solve this problem, we introduce a novel framework to achieve TC at any layer on the radio network stack.
CGNN: Traffic Classification with Graph Neural Network
Extensive evaluation over real-world traffic data sets, including normal, encrypted and malicious labels, show that, CGNN improves the prediction accuracy by 23\% to 29\% for application classification, by 2\% to 37\% for malicious traffic classification, and reaches the same accuracy level for encrypted traffic classification.
Time-Distributed Feature Learning in Network Traffic Classification for Internet of Things
The network traffic classification (NTC) is an essential tool to explore behaviours of network flows, and NTC is required for Internet service providers (ISPs) to manage the performance of the IoT network.
