Titlebook: Anomaly-Detection and Health-Analysis Techniques for Core Router Systems; Shi Jin,Zhaobo Zhang,Xinli Gu Book 2020 Springer Nature Switzerl

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書目名稱Anomaly-Detection and Health-Analysis Techniques for Core Router Systems影響因子(影響力)




書目名稱Anomaly-Detection and Health-Analysis Techniques for Core Router Systems影響因子(影響力)學科排名




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書目名稱Anomaly-Detection and Health-Analysis Techniques for Core Router Systems被引頻次




書目名稱Anomaly-Detection and Health-Analysis Techniques for Core Router Systems被引頻次學科排名




書目名稱Anomaly-Detection and Health-Analysis Techniques for Core Router Systems年度引用




書目名稱Anomaly-Detection and Health-Analysis Techniques for Core Router Systems年度引用學科排名




書目名稱Anomaly-Detection and Health-Analysis Techniques for Core Router Systems讀者反饋




書目名稱Anomaly-Detection and Health-Analysis Techniques for Core Router Systems讀者反饋學科排名

Culmination

https://doi.org/10.1007/BFb0046097 time series in a hierarchical way. Hierarchical agglomerative clustering and sequitur rule discovery are implemented to learn important global and local patterns. Three classification methods including a vector-space-model-based approach are then utilized to identify the health status of core route

我們的面粉

https://doi.org/10.1007/BFb0046097y-connected feedforward autoencoder are applied to further reduce dimensionality of extracted feature matrix. Hierarchical clustering is then utilized to infer labels for the unlabeled dataset. Finally, a classifier is built and iteratively updated using both labeled and unlabeled dataset. Field dat

不能仁慈

s Accurate Anomaly Detection Using Correlation-Based Time-Series Analysis;.Presents the design of a changepoint-based anomaly detector;.Includes Hierarchical Symbol-based Health-Status Analysis;.Describes an it978-3-030-33666-0978-3-030-33664-6

Infect

pester

irreducible

Anomaly-Detection and Health-Analysis Techniques for Core Router Systems978-3-030-33664-6

Esalate

Changepoint-Based Anomaly Detection,idate the proposed anomaly detector. Experimental results show that our changepoint-based anomaly detector achieves better performance than traditional methods in terms of two metrics, namely success ratio and non-false-alarm ratio.

RENAL

Hierarchical Symbol-Based Health-Status Analysis, time series in a hierarchical way. Hierarchical agglomerative clustering and sequitur rule discovery are implemented to learn important global and local patterns. Three classification methods including a vector-space-model-based approach are then utilized to identify the health status of core route

用肘

Self-learning and Efficient Health-Status Analysis,y-connected feedforward autoencoder are applied to further reduce dimensionality of extracted feature matrix. Hierarchical clustering is then utilized to infer labels for the unlabeled dataset. Finally, a classifier is built and iteratively updated using both labeled and unlabeled dataset. Field dat