A network security situational awareness model based on multi-source heterogeneous sensors

Xiaobo Tan; Guangjie Zhang

doi:10.1117/12.2675117

25 May 2023 A network security situational awareness model based on multi-source heterogeneous sensors

Xiaobo Tan, Guangjie Zhang

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 126364L (2023) https://doi.org/10.1117/12.2675117
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

In the research of network security situational awareness, an important problem that needs to be solved urgently is that in the face of a large number of noisy multi-source data, the accuracy of obtaining network security situational awareness indicators will be affected. This paper proposes a data fusion processing method based on deep neural network to solve the above problems, which first uses natural language processing technology to process high-noise data in the data, and does a good job of word reduction of the data through the WordNetLemmatizer module. The TF-IDF feature extraction algorithm is applied to extract features through the frequency of data occurrence and the weight of the data. The experimental results show that the accuracy of obtaining network security situational awareness indicators by using the data fusion method based on deep neural network reaches more than 85% compared with the data fusion methods of other machine learning algorithms, and can be applied to the network security situational awareness model.

Citation Download Citation

Xiaobo Tan and Guangjie Zhang "A network security situational awareness model based on multi-source heterogeneous sensors", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 126364L (25 May 2023); https://doi.org/10.1117/12.2675117

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