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<p>In recent decades, significant research effort has been put into developing solutions to support automatized or semi-automatized analysis of log files. A large number of algorithms appeared based on neural networks. In this paper we introduce a new approach to anomaly detection in log files, that does not rely on neural networks. The building blocks of our approach have been well-known in machine learning for a long time. We propose to use a weighted Damerau-Levenshtein distance metric to quantify the similarity between log sequences. We introduce a kNN-based algorithm for semi-supervised log anomaly detection, and an HDBSCAN-based solution for the unsupervised problem. For the latter, we extend the algorithm by incorporating a manual feedback mechanism, enabling human domain experts to modify sequence labels when necessary.</p>

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Detecting anomalies in log files using the Damerau-Levenshtein distance metric

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月10日

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  • Gábor Horváth,
  • András Mészáros,
  • Kamel Charaf,
  • Péter Szilágyi

摘要

In recent decades, significant research effort has been put into developing solutions to support automatized or semi-automatized analysis of log files. A large number of algorithms appeared based on neural networks. In this paper we introduce a new approach to anomaly detection in log files, that does not rely on neural networks. The building blocks of our approach have been well-known in machine learning for a long time. We propose to use a weighted Damerau-Levenshtein distance metric to quantify the similarity between log sequences. We introduce a kNN-based algorithm for semi-supervised log anomaly detection, and an HDBSCAN-based solution for the unsupervised problem. For the latter, we extend the algorithm by incorporating a manual feedback mechanism, enabling human domain experts to modify sequence labels when necessary.