Network Fault Root Cause Analysis Algorithm Based on Deep Learning

Liang, Yuxuan; Xu, Xiaowei; Xiao, Wei; Chen, Yingyang; Dai, Shiyue; Yu, Jun; Rui, Lanlan

doi:10.1007/978-981-19-6901-0_37

Cited by 1 publication

(1 citation statement)

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“…Root cause analysis (RCA), a task to to identify the underlying factors or events that trigger a network failure and determine the appropriate remedial actions, has been considered as a critical process in troubleshooting communication network failures [1] Conventional RCA methods typically rely on manual analysis or rule-based algorithms, which are expensive, errorprone, and often struggle to capture the complexity and dynamic nature of modern communication networks. Therefore, researchers have proposed various automated methods to assist in RCA, including statistical methods [2], [3], machine learning [4]- [7], and deep learning [8], [9]. Among them, deep learning has been proven to be a powerful tool in a variety of applications, including image classification [10], natural language processing [11], and speech recognition [12].…”

Section: Introductionmentioning

confidence: 99%

Root Cause Analysis of Communication Network Based on Deep Fuzzy Neural Network

Zhang

2023

IEEE Access

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In the realm of communication networks, root cause analysis plays a vital role in maintaining efficient and reliable operation. However, existing root cause analysis methods face limitations and drawbacks, including their inability to handle complex data and disturbances, as well as inaccuracies in identifying root causes. To this end, we present the Deep Fuzzy Neural Network approach as an innovative solution.Integrating the strengths of deep learning and fuzzy logic inference, where the deep learning technique utilizes the parallel computing fusion of convolutional neural network and long short-term memory to extract the spatial-temporal features from sophisticated fault data of communication network. By leveraging this parallel computing fusion module, the proposed framework effectively addresses the flaws of traditional root cause analysis methods. Furthermore, the incorporation of fuzzy logic enables our model to manage disturbances such as uncertainty and noise inherent in the data, ensuring robust performance. Experimental results also demonstrate our proposed deep fuzzy neural network approach is an effective method for network root cause analysis in overcoming limitations inherent in existing methods and providing superior accuracy and resilience.

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Section: Introductionmentioning

confidence: 99%

Root Cause Analysis of Communication Network Based on Deep Fuzzy Neural Network

Zhang

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

Network Fault Root Cause Analysis Algorithm Based on Deep Learning

Cited by 1 publication

References 3 publications

Root Cause Analysis of Communication Network Based on Deep Fuzzy Neural Network

Root Cause Analysis of Communication Network Based on Deep Fuzzy Neural Network

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