Neural Network-Based Log Analysis Methods for 5G Network.
Symposium on Intelligent Systems and Informatics（2023）
摘要
Log analysis plays a crucial role in understanding system behavior and detecting anomalies in various domains. This article presents the results of our autoencoder type neural network trained for log analysis and anomaly detection based on the reconstruction error of each entry. Furthermore, the article focuses on the challenge of text vectorization, specifically employing a bag-of-words method for representing log messages in a numerical format suitable for neural network models. The bag-of-words approach captures the frequency-based representation of log messages. The study also acknowledges the limitations of this method in capturing semantic information. It is important to note that this study represents only a starting point in the journey of log analysis and text vectorization. The field encompasses a wide range of existing working methods, and further research and exploration are necessary to uncover the full potential of log analysis techniques for various applications.
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关键词
Network-based Methods,5G Networks,Neural Network-based Methods,Neural Network-based Analysis,Neural Network,Autoencoder,Anomaly Detection,Deep Learning,Image Processing,Learning Algorithms,Convolutional Neural Network,Classification Task,Internet Of Things,Time Series Analysis,Long Short-term Memory,Recurrent Neural Network,Data Logger,Manual Inspection,Transformer Model,User Equipment,Concept Drift,Registration Process,Cybersecurity,Pattern Mining,Textual Messages,Rule-based Methods,Traditional Analysis Methods,Speech Processing,Neural Network-based Approach
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