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  3. A Comprehensive Study Of Machine Learning Techniques For Log-based Anomaly Detection.
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  3. A Comprehensive Study Of Machine Learning Techniques For Log-based Anomaly Detection.

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A comprehensive study of machine learning techniques for log-based anomaly detection.

Shan Ali1, Chaima Boufaied2, Domenico Bianculli3

  • 1University of Ottawa, Ottawa, Canada.

Empirical Software Engineering
|June 26, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Traditional and deep machine learning (ML) methods show similar performance for log-based anomaly detection (LAD). Traditional ML techniques are less sensitive to hyperparameter tuning than deep learning methods.

Keywords:
Anomaly detectionDeep learningLogMachine learning

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Software Engineering

Background:

  • System complexity necessitates automated log analysis techniques like Log-based Anomaly Detection (LAD).
  • Deep learning methods dominate LAD research, but traditional and semi-supervised approaches also warrant consideration.
  • Current evaluations of LAD techniques primarily focus on detection accuracy, neglecting crucial practical aspects.

Purpose of the Study:

  • To conduct a comprehensive empirical study comparing various supervised and semi-supervised, traditional and deep ML techniques for LAD.
  • To evaluate LAD techniques based on detection accuracy, time performance, and sensitivity to hyperparameter tuning.
  • To provide robust evidence on the relative strengths and weaknesses of different ML approaches for LAD.

Main Methods:

  • Evaluation of a wide array of supervised and semi-supervised, traditional and deep ML techniques.
  • Assessment across four criteria: detection accuracy, training time, prediction time, and sensitivity to hyperparameter tuning.
  • Empirical comparison using benchmark datasets relevant to LAD.

Main Results:

  • Supervised traditional and deep ML techniques exhibit comparable detection accuracy and prediction times on most benchmark datasets.
  • Traditional ML techniques demonstrate lower sensitivity to hyperparameter tuning compared to deep learning methods.
  • Semi-supervised techniques generally achieve significantly lower detection accuracy than supervised methods.

Conclusions:

  • Both traditional and deep supervised ML techniques are viable for LAD, with traditional methods offering better hyperparameter robustness.
  • The choice of LAD technique should consider not only accuracy but also computational performance and sensitivity to tuning.
  • Further research should explore the practical implications of these findings for system engineers.