LogDTL: Network Log Template Generation with Deep Transfer Learning

Analyzing network logs is increasingly playing an essential role in system management and maintenance. As a result, more and more new techniques and models have been proposed for automatic log analysis. Log template generation is the essential first step to apply such sophisticated techniques. This article presents an automatic log template generation framework (LogDTL) in which transfer learning technique is used in the deep neural network (DTNN model) to overcome the trade-off between the accuracy of the generated template and human resources for manual labeling. Our evaluation results show that DTNN significantly outperforms a well-known supervised method (CRF). DTNN achieves 91% of word accuracy with only one training example though the CRF achieves 78% of word accuracy.