Traffic Classification
18 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
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.
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 First Look at Class Incremental Learning in Deep Learning Mobile Traffic Classification
The recent popularity growth of Deep Learning (DL) re-ignited the interest towards traffic classification, with several studies demonstrating the accuracy of DL-based classifiers to identify Internet applications' traffic.
Efficient Detection of Botnet Traffic by features selection and Decision Trees
Botnets are one of the online threats with the biggest presence, causing billionaire losses to global economies.
Active Learning for Network Traffic Classification: A Technical Study
Machine Learning (ML) algorithms as a popular approach for NTC can promise reasonable accuracy in classification and deal with encrypted traffic.
Evaluating Resilience of Encrypted Traffic Classification Against Adversarial Evasion Attacks
Machine learning and deep learning algorithms can be used to classify encrypted Internet traffic.
Flow-Packet Hybrid Traffic Classification for Class-Aware Network Routing
At a network router, the packets need to be processed with minimum delay, so the classifier cannot wait until the end of the flow to make a decision.
Federated Traffic Synthesizing and Classification Using Generative Adversarial Networks
With the fast growing demand on new services and applications as well as the increasing awareness of data protection, traditional centralized traffic classification approaches are facing unprecedented challenges.
Applications of Artificial Intelligence, Machine Learning and related techniques for Computer Networking Systems
This article presents a primer/overview of applications of Artificial Intelligence and Machine Learning (AI/ML) techniques to address problems in the domain of computer networking.
Deep Learning and Traffic Classification: Lessons learned from a commercial-grade dataset with hundreds of encrypted and zero-day applications
The increasing success of Machine Learning (ML) and Deep Learning (DL) has recently re-sparked interest towards traffic classification.
