On the fly classification of traffic in Anonymous Communication Networks using a Machine Learning approach

Chinmayee, Lalitha; Hurali, Lalitha Chinmayee M.; Patil, Annapurna P.

doi:10.1109/ants50601.2020.9342804

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2023

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Cited by 2 publications

References 11 publications

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A Review on Garlic Routing and Artificial Intelligence Applications in Public Network

S.,

Thangavel,

Basheer

2023

2023 International Conference on Computer Science and Emerging Technologies (CSET)

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A Review on Garlic Routing and Artificial Intelligence Applications in Public Network

S.,

Thangavel,

Basheer

2023

2023 International Conference on Computer Science and Emerging Technologies (CSET)

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An Effective Real-time Traffic Classification Method Using Convolutional Neural Network

Yang,

Wang,

Feng

et al. 2023

Preprint

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Network traffic classification has been as a research hots pot in network studies. However, previous research has predominantly focused on coarse-grained classification, neglecting fine-grained classification among network flows. As the increasing demand for personalized network services, fine-grained classification of network flows research has become imminently. This study discusses the task of fine-grained classification mainly, specifically for chat flows. We proposed a Convolutional Neural Network (CNN)-based method for fine-grained real-time classification of chat flows. Firstly, we pre-process the five-tuple data, analysis the probabilistic feature values about the protocols by first-order Markov chains, then using the features as input data of CNN model. Secondly, we propose an improved adaptive step method to optimize the training CNN model; Additionally, we combining the bagging algorithm with the CNN model to improve its classification performance. To validate the effectiveness of our proposed method, we conducted experiments using two different chat flows from the ISCX database. The experiment results show that the proposed classification method effectively improved the fine-grained traffic classification results. It increases the classification accuracy of non-VPN chat flows from 76.7% and 80.8% to 88.8% and that of VPN chat flows from 91.0% and 93.6% to 97.9%.

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On the fly classification of traffic in Anonymous Communication Networks using a Machine Learning approach

Cited by 2 publications

References 11 publications

A Review on Garlic Routing and Artificial Intelligence Applications in Public Network

A Review on Garlic Routing and Artificial Intelligence Applications in Public Network

An Effective Real-time Traffic Classification Method Using Convolutional Neural Network

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