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DOI: 10.4230/OASIcs.SLATE.2024.12
Anomaly Detection in Logs: A Comparative Analysis of Unsupervised Algorithms

Authors: Alysson C. E. de Moura, Geraldo P. Rocha Filho, Marcos F. Caetano, João J. C. Gondim, Aleteia Araujo, Marcelo A. Marotta, and Lucas Bondan

Published in: OASIcs, Volume 120, 13th Symposium on Languages, Applications and Technologies (SLATE 2024)

Abstract
This study explores anomaly detection through unsupervised Machine Learning applied to banking systems' log records. The diversity in formatting and types of logs poses significant challenges for automating anomaly detection. We propose a workflow using Natural Language Processing (NLP) techniques for anomaly identification, which in further analysis can lead to identifying root causes of failures and vulnerabilities. We evaluate the performance of eight different models using Blue Gene/L log records. The most effective models were selected and subsequently validated with Microsoft Configuration Manager (MCM) logs collected from a financial institution, demonstrating their practical applicability in real-world scenarios. Experimental results highlighted the effectiveness of neural network models, specifically Self-Organizing Maps (SOM) and Autoencoders (AE), with F1-Scores of 0.86 and 0.80, respectively, when applied to MCM logs collected from the financial institution.
Cite as

Alysson C. E. de Moura, Geraldo P. Rocha Filho, Marcos F. Caetano, João J. C. Gondim, Aleteia Araujo, Marcelo A. Marotta, and Lucas Bondan. Anomaly Detection in Logs: A Comparative Analysis of Unsupervised Algorithms. In 13th Symposium on Languages, Applications and Technologies (SLATE 2024). Open Access Series in Informatics (OASIcs), Volume 120, pp. 12:1-12:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{demoura_et_al:OASIcs.SLATE.2024.12,
  author =	{de Moura, Alysson C. E. and Filho, Geraldo P. Rocha and Caetano, Marcos F. and Gondim, Jo\~{a}o J. C. and Araujo, Aleteia and Marotta, Marcelo A. and Bondan, Lucas},
  title =	{{Anomaly Detection in Logs: A Comparative Analysis of Unsupervised Algorithms}},
  booktitle =	{13th Symposium on Languages, Applications and Technologies (SLATE 2024)},
  pages =	{12:1--12:14},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-321-8},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{120},
  editor =	{Rodrigues, M\'{a}rio and Leal, Jos\'{e} Paulo and Portela, Filipe},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2024.12},
  URN =		{urn:nbn:de:0030-drops-220831},
  doi =		{10.4230/OASIcs.SLATE.2024.12},
  annote =	{Keywords: Anomaly Detection, Log Analysis, Natural Language Processing, Unsupervised Learning, Word Embeddings}
}
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