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- W4200520462 abstract "This paper analyses deep learning (DL) techniques for network traffic identification. Various DL models have been presented, implemented, and compared, including 1D convolutional, stacked autoencoder, multi-layer perceptron, and a combination of the aforementioned. The real dataset including both encrypted and unencrypted traffic has been collected, pre-processed in a way that ensures non-biased results, and used to train and test DL models. The models have been compared in terms of precision, recall, F1 score and accuracy for different levels of complexity. The obtained results suggest that the best performance is achieved with neural networks using a combination of 1D convolutional layers and fully-connected layers." @default.
- W4200520462 created "2021-12-31" @default.
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- W4200520462 date "2021-11-23" @default.
- W4200520462 modified "2023-09-27" @default.
- W4200520462 title "Performance Analysis of Deep Learning Models for Network Traffic Identification" @default.
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- W4200520462 doi "https://doi.org/10.1109/telfor52709.2021.9653205" @default.
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