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Temporal Decay Loss for Adaptive Log Anomaly Detection in Cloud Environments.

Lelisa Adeba Jilcha1, Deuk-Hun Kim2, Jin Kwak3

  • 1ISAA Laboratory, Department of AI Convergence Network, Ajou University, Suwon 16499, Republic of Korea.

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|May 14, 2025
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Summary

This study introduces a novel approach for log anomaly detection in cloud environments using a domain-specific pre-trained language model and a Loss with Decaying Factor (LDF). This method enhances zero-shot transfer performance by balancing historical data with real-time relevance.

Keywords:
LDFadaptive detectionanomaly detectioncloud computinglog preprocessingpretrained language modeltemporal decay losstemporal dependencyzero-shot detection

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cybersecurity

Background:

  • Log anomaly detection is crucial for cloud system reliability and security.
  • Traditional sequence models struggle with zero-shot transfer due to data shifts.
  • Existing methods often overemphasize outdated information and incur high computational costs.

Purpose of the Study:

  • To develop an effective log anomaly detection approach for zero-shot transfer scenarios in cloud computing.
  • To address challenges posed by distributional shifts and semantic discrepancies across heterogeneous log datasets.
  • To improve the generalization capability of anomaly detection models in dynamic cloud environments.

Main Methods:

  • Integration of a domain-specific pre-trained language model (PLM) fine-tuned on cybersecurity data.
  • Introduction of a novel Loss with Decaying Factor (LDF) with an exponential time decay mechanism.
  • Dynamic weighting of log messages based on temporal proximity to balance historical and real-time data.

Main Results:

  • Substantial enhancement in cross-dataset anomaly detection performance demonstrated through empirical evaluations.
  • Improved representation of log data across heterogeneous datasets via the domain-specific PLM.
  • Mitigation of semantic discrepancies and better alignment with evolving cloud environments.

Conclusions:

  • The proposed approach significantly improves zero-shot log anomaly detection in heterogeneous cloud environments.
  • The combination of a domain-specific PLM and LDF effectively addresses limitations of traditional sequence models.
  • This method offers a more dynamic and relevant approach to anomaly detection in rapidly changing systems.