Abstract:

Finding unusual traffic is essential for both network security and service quality. A big-step convolutional neural network traffic detection model based on the attention mechanism is developed since the single dimension of the detection model and feature similarity make it extremely difficult to identify anomalous traffic. First, the raw traffic is preprocessed and mapped into a two-dimensional greyscale picture after the network traffic characteristics are examined. After that, histogram equalisation is used to create multi-channel greyscale pictures, and an attention mechanism is added to give traffic features varying weights in order to improve local features. Lastly, traffic characteristics of various depths are extracted by combining pooling-free convolutional neural networks, which improves convolutional neural network flaws including overfitting and local feature omission. A balanced public data set and an actual data set were used for the simulation experiment.

Description:

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