Online Traffic Classification Using Granules

With the rising demand for wireless services and increased awareness of the need for data protection, existing network traffic analysis and management architectures are facing unprecedented challenges in classifying and synthesizing the increasingly diverse services and applications. This paper proposes FS-GAN, a federated self-supervised learning framework to support automatic traffic analysis and synthesis over a large number of heterogeneous datasets. FS-GAN is composed of multiple distributed Generative Adversarial Networks (GANs), with a set of generators, each being designed to generate synthesized data samples following the distribution of an individual service traffic, and each discriminator being trained to differentiate the synthesized data samples and the real data samples of a local dataset. A federated learning-based framework is adopted to coordinate local model training processes of different GANs across different datasets. FS-GAN can classify data of unknown types of service and create synthetic samples that capture the traffic distribution of the unknown types. We prove that FS-GAN can minimize the Jensen-Shannon Divergence (JSD) between the distribution of real data across all the datasets and that of the synthesized data samples. FS-GAN also maximizes the JSD among the *This work has been accepted at IEEE Transactions on Mobile Computing.

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“…Deep neural network (DNNs), are one of the promising solutions for network traffic classification [15], [17], [27]- [34]. [28].…”

Section: Deep-learning-based Traffic Classificationmentioning

confidence: 99%

Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-Supervised Learning Approach

Xiao

Xia

et al. 2024

IEEE Trans. on Mobile Comput.

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“…At frst, we introduce the concept of "fow granule." Te concept of "fow granule" used in this paper is inspired and derived from [26]. Te term "fow granule" was initially used in [26], which explained how neighborhood data packets with the same packet length might be aggregated to create granules.…”

Section: Graph Generation Based On Granules In Edge Nodesmentioning

confidence: 99%

“…Te concept of "fow granule" used in this paper is inspired and derived from [26]. Te term "fow granule" was initially used in [26], which explained how neighborhood data packets with the same packet length might be aggregated to create granules. As a result, an aggregated packet sequence rather than a single, unique packet now represents the information to be processed.…”

Section: Graph Generation Based On Granules In Edge Nodesmentioning

confidence: 99%

Semisupervised Graph Neural Networks for Traffic Classification in Edge Networks

Yang

Lyu

Gao

et al. 2023

Discrete Dynamics in Nature and Society

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Edge networking brings computation and data storage as close to the point of request as possible. Various intelligent devices are connected to the edge nodes where traffic packets flow. Traffic classification tasks are thought to be a keystone for network management; researchers can analyze packets captured to understand the traffic as it hits their network. However, the existing traffic classification framework needs to conduct a unified analysis, which leads to the huge bandwidth resources required in the process of transferring all captured packet files to train a global classifier. In this paper, a semisupervised graph neural network traffic classifier is proposed for cloud-edge architecture so that cloud servers and edge nodes could cooperate to perform the traffic classification tasks in order to deliver low latency and save bandwidth on the edge nodes. To preserve the structural information and interrelationships conveyed in packets within a session, we transform traffic sessions into graphs. We segment the frequently combined consecutive packets into granules, which are later transformed into the nodes in graphs. Edges could extract the adjacency of the granules in the sessions; the edge node side then selects the highly representative samples and sends them to the cloud server; the server side uses graph neural networks to perform semisupervised classification tasks on the selected training set. Our method has been trained and tested on several datasets, such as the VPN-nonVPN dataset, and the experimental results show good performance on accuracy, recall, and F-score.

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Real-Time Platform Identification of VPN Video Streaming Based on Side-Channel Attack

Lu,

Wu,

Luo

et al. 2024

IFIP Advances in Information and Communication Technology

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Online Traffic Classification Using Granules

Cited by 8 publications

References 24 publications

Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-Supervised Learning Approach

Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-Supervised Learning Approach

Semisupervised Graph Neural Networks for Traffic Classification in Edge Networks

Real-Time Platform Identification of VPN Video Streaming Based on Side-Channel Attack

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