Traffic Classification
16 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
Latest papers with no code
CBR - Boosting Adaptive Classification By Retrieval of Encrypted Network Traffic with Out-of-distribution
To summarize, the new method is a real-time classification, which can classify new classes without retraining.
Deep Learning Approaches for Network Traffic Classification in the Internet of Things (IoT): A Survey
The Internet of Things (IoT) has witnessed unprecedented growth, resulting in a massive influx of diverse network traffic from interconnected devices.
Data Augmentation for Traffic Classification
Data Augmentation (DA) -- enriching training data by adding synthetic samples -- is a technique widely adopted in Computer Vision (CV) and Natural Language Processing (NLP) tasks to improve models performance.
netFound: Foundation Model for Network Security
In ML for network security, traditional workflows rely on high-quality labeled data and manual feature engineering, but limited datasets and human expertise hinder feature selection, leading to models struggling to capture crucial relationships and generalize effectively.
Toward Generative Data Augmentation for Traffic Classification
Data Augmentation (DA)-augmenting training data with synthetic samples-is wildly adopted in Computer Vision (CV) to improve models performance.
Towards Intelligent Network Management: Leveraging AI for Network Service Detection
As the complexity and scale of modern computer networks continue to increase, there has emerged an urgent need for precise traffic analysis, which plays a pivotal role in cutting-edge wireless connectivity technologies.
Genetic Algorithm-Based Dynamic Backdoor Attack on Federated Learning-Based Network Traffic Classification
In this paper, we propose GABAttack, a novel genetic algorithm-based backdoor attack against federated learning for network traffic classification.
NetDiffus: Network Traffic Generation by Diffusion Models through Time-Series Imaging
Network data analytics are now at the core of almost every networking solution.
Listen to Minority: Encrypted Traffic Classification for Class Imbalance with Contrastive Pre-Training
Despite some existing learning-based ETC methods showing promising results, three-fold limitations still remain in real-world network environments, 1) label bias caused by traffic class imbalance, 2) traffic homogeneity caused by component sharing, and 3) training with reliance on sufficient labeled traffic.
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.