Encrypted Traffic Identification Method Based on Multi-scale Spatiotemporal Feature Fusion Model with Attention Mechanism

Huo, Yonghua; Ge, Hongwu; Jiao, Libin; Gao, Bowen; Yang, Yang

doi:10.1007/978-981-16-6554-7_92

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…E. Hodo et al [15] used an artificial neural network and a support vector machine for binary classification of the public dataset ISCXTor2016 and achieved satisfactory accuracies. Huo, Y et al [16]. proposed a new classification model.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

AE-DTI: An Efficient Darknet Traffic Identification Method Based on Autoencoder Improvement

Yang,

Jiang,

Deng

et al. 2023

Applied Sciences

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With the continuous expansion of the darknet and the increase in various criminal activities in the darknet, darknet traffic identification has become increasingly essential. However, existing darknet traffic identification methods rely on all traffic characteristics, which require a long computing time and a large amount of system resources, resulting in low identification efficiency. To this end, this paper proposes an autoencoder-based darknet traffic identification method (AE-DTI). First, AE-DTI maps the feature values to pixels of a two-dimensional grayscale image after deduplication and denoising of the darknet traffic dataset. Then, AE-DTI designs a new feature selection algorithm (AE-FS) to downscale the grayscale graph, and AE-FS trains a feature scoring network, which globally scores all the features based on the reconstruction error to select the features with scores greater than or equal to a set threshold value. Finally, AE-DTI uses a one-dimensional convolutional neural network with a dropout layer to identify darknet traffic on the basis of alleviating overfitting. Experimental results on the ISCXTor2016 dataset show that, compared with other dimensionality reduction methods (PCA, LLE, ISOMAP, and autoencoder), the classification model trained with the data obtained from AE-FS has a significant improvement in classification accuracy and classification efficiency. Moreover, AE-DTI also shows significant improvement in recognition accuracy compared with other models. Experimental results on the CSE-CIC-IDS2018 dataset and CIC-Darknet2020 dataset show that AE-DTI has strong generalization.

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Section: Deep Learning Methodsmentioning

confidence: 99%

AE-DTI: An Efficient Darknet Traffic Identification Method Based on Autoencoder Improvement

Yang,

Jiang,

Deng

et al. 2023

Applied Sciences

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“…Huo et al [16] noted that a large number of parameters need to be calculated to train a network to classify Tor traffic. As calculating these parameters is computationally expensive, they propose a new model that extracts spatial features by CNN layers, gathers temporal features from LSTM layers, then fuses multi-scale features before sending the features to an attention mechanism.…”

Section: B Tor Traffic Detectionmentioning

confidence: 99%

Evaluation of Synthetic Data Generation Techniques in the Domain of Anonymous Traffic Classification

et al. 2022

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Anonymous network traffic is more pervasive than ever due to the accessibility of services such as virtual private networks (VPN) and The Onion Router (Tor). To address the need to identify and classify this traffic, machine and deep learning solutions have become the standard. However, highperforming classifiers often scale poorly when applied to real-world traffic classification due to the heavily skewed nature of network traffic data. Prior research has found synthetic data generation to be effective at alleviating concerns surrounding class imbalance, though a limited number of these techniques have been applied to the domain of anonymous network traffic detection. This work compares the ability of a Conditional Tabular Generative Adversarial Network (CTGAN), Copula Generative Adversarial Network (CopulaGAN), Variational Autoencoder (VAE), and Synthetic Minority Over-sampling Technique (SMOTE) to create viable synthetic anonymous network traffic samples. Moreover, we evaluate the performance of several shallow boosting and bagging classifiers as well as deep learning models on the synthetic data. Ultimately, we amalgamate the data generated by the GANs, VAE, and SMOTE into a comprehensive dataset dubbed CMU-SynTraffic-2022 for future research on this topic. Our findings show that SMOTE consistently outperformed the other upsampling techniques, improving classifiers' F1-scores over the control by ~7.5% for application type characterization. Among the tested classifiers, Light Gradient Boosting Machine achieved the highest F1-score of 90.3% on eight application types.

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FEAML: A Mobile Traffic Classification System with Feature Expansion and Autonomous Machine Learning

Yang,

Kong,

Xiao

et al. 2024

Lecture Notes in Computer Science

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Encrypted Traffic Identification Method Based on Multi-scale Spatiotemporal Feature Fusion Model with Attention Mechanism

Cited by 4 publications

References 5 publications

AE-DTI: An Efficient Darknet Traffic Identification Method Based on Autoencoder Improvement

AE-DTI: An Efficient Darknet Traffic Identification Method Based on Autoencoder Improvement

Evaluation of Synthetic Data Generation Techniques in the Domain of Anonymous Traffic Classification

FEAML: A Mobile Traffic Classification System with Feature Expansion and Autonomous Machine Learning

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