Abstract
Aiming at the problem that the current network intrusion behavior is poorly detected and the low data utilization rate in the big data environment, two-layer ensemble model based on the stacking framework is proposed to detect the network intrusion data. First, the PCA algorithm is used in the model to reduce the dimensionality of the data, before constructing a two-layer ensemble model. A variety of existing ensemble classifiers are used in the first layer of the model as the base classifier and the simple classifier are used in the second layer to train the classification results of the first layer. Thereby the final prediction result of the model is obtained. According to the characteristics of network intrusion big data, the base classifier can be selected autonomously and the number of classifiers can be optimized spontaneously, and the scale of the classifier can be adjusted to obtain most optimized classier. The experiment result shows: (1) Dynamic selection by data feature, the optimal ensemble base classifier in the model can be determined. (2) According to the data characteristics, the number of ensemble-based classifiers is determined, which can improve the performance of the classifier under the premise of ensuring classification accuracy, and reduce the redundancy caused by the integration of unnecessary base classifiers. (3) This model can stably improve the accuracy of intrusion detection.
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Feng, T., Dou, M., Xie, P., Fang, J. (2020). Network Intrusion Detection Based on Data Feature Dynamic Ensemble Model. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Communications in Computer and Information Science, vol 1253. Springer, Singapore. https://doi.org/10.1007/978-981-15-8086-4_62
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DOI: https://doi.org/10.1007/978-981-15-8086-4_62
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