A Pipeline of Augmentation and Sequence Embedding for Classification of Imbalanced Network Traffic

• URL: http://arxiv.org/abs/2502.18909v1
• Date: Wed, 26 Feb 2025 07:55:24 GMT
• Title: A Pipeline of Augmentation and Sequence Embedding for Classification of Imbalanced Network Traffic
• Authors: Matin Shokri, Ramin Hasibi,
• Abstract summary: We propose a pipeline to balance the dataset and classify it using a robust and accurate embedding technique.<n>We demonstrate that the proposed augmentation pipeline, combined with FS-Embedding, increases convergence speed and leads to a significant reduction in the number of model parameters.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Network Traffic Classification (NTC) is one of the most important tasks in network management. The imbalanced nature of classes on the internet presents a critical challenge in classification tasks. For example, some classes of applications are much more prevalent than others, such as HTTP. As a result, machine learning classification models do not perform well on those classes with fewer data. To address this problem, we propose a pipeline to balance the dataset and classify it using a robust and accurate embedding technique. First, we generate artificial data using Long Short-Term Memory (LSTM) networks and Kernel Density Estimation (KDE). Next, we propose replacing one-hot encoding for categorical features with a novel embedding framework based on the "Flow as a Sentence" perspective, which we name FS-Embedding. This framework treats the source and destination ports, along with the packet's direction, as one word in a flow, then trains an embedding vector space based on these new features through the learning classification task. Finally, we compare our pipeline with the training of a Convolutional Recurrent Neural Network (CRNN) and Transformers, both with imbalanced and sampled datasets, as well as with the one-hot encoding approach. We demonstrate that the proposed augmentation pipeline, combined with FS-Embedding, increases convergence speed and leads to a significant reduction in the number of model parameters, all while maintaining the same performance in terms of accuracy.

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