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Using log sequence representations and transfer learning in classification and novelty detection
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School of Science |
Master's thesis
Electronic archive copy is available via Aalto Thesis Database.
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Mcode
Language
en
Pages
74
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Abstract
The goal of this study is to experiment with a method that uses log sequence representations and transfer learning to perform multiple downstream tasks in analysing logs, including classification and novelty detection. The proposed method is evaluated on two log datasets with distinct properties, where both the transfer learning approach and traditional time-series models as baselines are used and compared.
The results show that the proposed method is comparable to or outperforming the baseline methods in the classification task, and it outperforms the baseline methods in scenarios of limited training data. The study also provide suggestions on the choice of methods depending on the properties of the log data, as well as approaches to improve the performance in terms of data-loading mechanisms and fine-tuning approaches.
In addition, the proposed method serves as a simplified approach in the novelty detection task to detect novelties in the log data, which indicates great potential for the proposed method.
In conclusion, this study shows that the proposed method is a promising approach for analysing logs in both classification and novelty detection tasks.