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- W3204199156 abstract "The plethora of Internet of Things (IoT) devices leads to explosive network traffic. The network traffic classification (NTC) is an essential tool to explore behaviours of network flows, and NTC is required for Internet service providers (ISPs) to manage the performance of the IoT network. We propose a novel network data representation, treating the traffic data as a series of images. Thus, the network data is realized as a video stream to employ time-distributed (TD) feature learning. The intra-temporal information within the network statistical data is learned using convolutional neural networks (CNN) and long short-term memory (LSTM), and the inter pseudo-temporal feature among the flows is learned by TD multi-layer perceptron (MLP). We conduct experiments using a large data-set with more number of classes. The experimental result shows that the TD feature learning elevates the network classification performance by 10%." @default.
- W3204199156 created "2021-10-11" @default.
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- W3204199156 date "2021-09-29" @default.
- W3204199156 modified "2023-10-17" @default.
- W3204199156 title "Time-Distributed Feature Learning in Network Traffic Classification for Internet of Things" @default.
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- W3204199156 doi "https://doi.org/10.48550/arxiv.2109.14696" @default.
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