| 微服务架构下烟草信息化网络异常数据层次密度聚类检测算法 |
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| 引用本文：叶玉琼1，刘杰1，张文府1，杨虹1，钱怡君1，夏斌1，袁齐风2.微服务架构下烟草信息化网络异常数据层次密度聚类检测算法[J].计算技术与自动化,2025,(1):177-182 |
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| 中文摘要:当前对于烟草信息化网络异常数据检测主要依托于物联网技术，由于缺少对异常数据多重特征属性的分类，使得检测精度较低。为此，提出了微服务架构下烟草信息化网络异常数据层次密度聚类检测算法。通过计算异常数据样本到聚类中心的局部可达密度确定类簇中心，并根据聚类决策图对异常数据进行层次密度聚类识别，结合小波包函数对异常数据进行分解，根据属性特征将其归类到不同类簇下，并基于网络传输节点分布模型构建异常数据传输信道模型，以此为依据，求取异常数据的熵值，并将计算结果与固定阈值比较，将阈值外的数据确定为异常数据，进而实现异常数据检测。对比实验结果表明，所提方法对于烟草信息化网络异常数据具有较高的检测精度。 |
| 中文关键词:微服务架构 烟草信息化网络 异常数据 层次密度聚类检测 |
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| Hierarchical Density Clustering Detection Algorithm for Abnormal Data in Tobacco Informatization Network under Microservice Architecture |
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| Abstract:Currently, the detection of abnormal data in tobacco information networks mainly relies on internet of things technology. Due to the lack of classification of multiple feature attributes of abnormal data, the detection accuracy is low. To this end, a hierarchical density clustering detection algorithm for abnormal data in tobacco information network under microservice architecture is proposed. By calculating the local reachable density of abnormal data samples to the cluster center, the cluster center is determined, and hierarchical density clustering recognition is performed on the abnormal data based on the clustering decision diagram. The abnormal data is decomposed using wavelet packet function, classified into different clusters based on attribute characteristics, and an abnormal data transmission channel model is constructed based on the network transmission node distribution model. Based on this, the entropy value of the abnormal data is calculated, and compare the calculation results with a fixed threshold, determine the data outside the threshold as abnormal data, and then achieve abnormal data detection. The test results show that the designed method can detect abnormal network operation data with high accuracy, and the detection effect is better. |
| keywords:microservice architecture tobacco information network abnormal data hierarchical density clustering detection |
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