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LBNN: Perceiving the state changes of a core telecommunications network via linear bayesian neural network
Lin,Yanying1; Ye,Kejiang2; Chen,Ming2; Deng,Naitian1; Wu,Tailin1; Xu,Cheng Zhong3
2020-12-01
Conference Name26th IEEE International Conference on Parallel and Distributed Systems (IEEE ICPADS)
Source PublicationProceedings of the International Conference on Parallel and Distributed Systems - ICPADS
Volume2020-December
Pages72-80
Conference DateDEC 02-04, 2020
Conference PlaceELECTR NETWORK
Abstract

The core network is the most basic facility in the entire telecommunications network, which is consists of large number of routers, switches and firewalls. Network management like re-planning routes or adjusting policies is very important to avoid failures. However, the timing of intervention is very challenging. Too early intervention will incur unnecessary overheads, and too late intervention will cause serious disaster. In this paper, we analyzed a large data set from a real-world core telecommunications network and proposed Linear Bayesian Neural Networks (LBNN)11Code available at https://github.com/YanyingLin/Lbnn to perceive the core network state changes and make decisions about network intervention. In particular, we considered three aspects of complexity, including the weight of the mutual effect between devices, the dependence on the time dimension of the network states, and the randomness of the network state changes. The entire model is extended to a probability model based on the Bayesian framework to better capture the randomness and variability of the data. Experimental results on real-world data set show that LBNN achieves very high detection accuracy, with an average of 92.1%.

KeywordLinear Bayesian Neural Network State Change Perception Telecommunications Core Network
DOI10.1109/ICPADS51040.2020.00020
URLView the original
Indexed ByCPCI-S
Language英語English
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000662964400009
Scopus ID2-s2.0-85102353387
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionUniversity of Macau
Corresponding AuthorYe,Kejiang
Affiliation1.University of Chinese Academy of Sciences,Beijing,100049,China
2.Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences,Shenzhen,518055,China
3.State Key Lab of IoTSC,University of Macau,Faculty of Science and Technology,Macao
Recommended Citation
GB/T 7714
Lin,Yanying,Ye,Kejiang,Chen,Ming,et al. LBNN: Perceiving the state changes of a core telecommunications network via linear bayesian neural network[C],2020:72-80.
APA Lin,Yanying,Ye,Kejiang,Chen,Ming,Deng,Naitian,Wu,Tailin,&Xu,Cheng Zhong.(2020).LBNN: Perceiving the state changes of a core telecommunications network via linear bayesian neural network.Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS,2020-December,72-80.
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