Abstract
Network traffic text classification plays an important role in network security. Traditional classification methods based on machine learning, such as supervised learning algorithms and semi-supervised algorithms, are insufficient: classification mode is too simple, unable to adapt to diverse classification requirements; text feature selection method is simple, text classification lacks diversity, and classification accuracy is low. And the classification speed is slow, not suitable for environments with high traffic and real-time. Multi-instance learning classification can describe the characteristics of the sample more accurately and comprehensively, and can improve the classification effect. In this paper, we combined the multi-instance learning classification with principal component analysis (PCA) to select text features of data sets, and removed the redundant and uncorrelated features in the original data, obtained a better classification accuracy.
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Wang, H., Luo, Q., Shang, Z., Li, G., Shi, X. (2020). Network Traffic Text Classification Based on Multi-instance Learning and Principal Component Analysis. In: Liang, Q., Wang, W., Liu, X., Na, Z., Jia, M., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2019. Lecture Notes in Electrical Engineering, vol 571. Springer, Singapore. https://doi.org/10.1007/978-981-13-9409-6_307
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DOI: https://doi.org/10.1007/978-981-13-9409-6_307
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