Log anomaly detection via Meta Learning and Prototypical Networks for Cross domain generalization

• URL: http://arxiv.org/abs/2601.14336v1
• Date: Tue, 20 Jan 2026 12:09:33 GMT
• Title: Log anomaly detection via Meta Learning and Prototypical Networks for Cross domain generalization
• Authors: Krishna Sharma, Vivek Yelleti,
• Abstract summary: We propose a new end-to-end framework based on a meta-learning approach to detect log anomalies.<n>Our methodology first gets the data ready by combining a Drain3 log parsing mechanism with a dynamic drift-based labeling technique.<n>Model Agnostic Meta-Learning (MAML) and Prototypical Networks models are trained to adapt quickly and effectively.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Log anomaly detection is essential for system reliability, but it is extremely challenging to do considering it involves class imbalance. Additionally, the models trained in one domain are not applicable to other domains, necessitating the need for cross-domain adaptation (such as HDFS and Linux). Traditional detection models often fail to generalize due to significant data drift and the inherent absence of labeled anomalies in new target domains. To handle the above challenges, we proposed a new end-to-end framework based on a meta-learning approach. Our methodology first gets the data ready by combining a Drain3 log parsing mechanism with a dynamic drift-based labeling technique that uses semantic and fuzzy matching to move existing anomaly knowledge from one source to another. BERT-based semantic embeddings are obtained, and the feature selection is invoked to reduce the dimensionality. Later, Model Agnostic Meta-Learning (MAML) and Prototypical Networks models are trained to adapt quickly and effectively. The SMOTE oversampling method is employed to handle imbalances in the data. All the results are obtained by employing the leave-one-out source method, and the corresponding mean F1 scores are reported. Our empirical findings validate that the proposed meta-learning-driven approach yielded the highest mean F1 score and proved to be effective for cross-domain settings.

Related papers

• Domain-Shift Immunity in Deep Deformable Registration via Local Feature Representations [0.2864713389096699]
  We show that domain-shift is an inherent property of deep deformable registration models.<n>We introduce UniReg, a universal registration framework that decouples feature extraction from deformation estimation.<n>Despite training on a single dataset, UniReg exhibits robust cross-domain and multi-modal performance comparable to optimization-based methods.
  arXiv  Detail & Related papers  (2025-12-29T02:10:52Z)
• EReLiFM: Evidential Reliability-Aware Residual Flow Meta-Learning for Open-Set Domain Generalization under Noisy Labels [85.78886153628663]
  Open-Set Domain Generalization aims to enable deep learning models to recognize unseen categories in new domains.<n>Label noise hinders open-set domain generalization by corrupting source-domain knowledge.<n>We propose Evidential Reliability-Aware Residual Flow Meta-Learning (EReLiFM) to bridge domain gaps.
  arXiv  Detail & Related papers  (2025-10-14T16:23:11Z)
• CLIP Meets Diffusion: A Synergistic Approach to Anomaly Detection [49.11819337853632]
  Anomaly detection is a complex problem due to the ambiguity in defining anomalies, the diversity of anomaly types, and the scarcity of training data.<n>We propose CLIPfusion, a method that leverages both discriminative and generative foundation models.<n>We believe that our method underscores the effectiveness of multi-modal and multi-model fusion in tackling the multifaceted challenges of anomaly detection.
  arXiv  Detail & Related papers  (2025-06-13T13:30:15Z)
• DRIVE: Dual-Robustness via Information Variability and Entropic Consistency in Source-Free Unsupervised Domain Adaptation [10.127634263641877]
  Adapting machine learning models to new domains without labeled data is a critical challenge in applications like medical imaging, autonomous driving, and remote sensing.<n>This task, known as Source-Free Unsupervised Domain Adaptation (SFUDA), involves adapting a pre-trained model to a target domain using only unlabeled target data.<n>Existing SFUDA methods often rely on single-model architectures, struggling with uncertainty and variability in the target domain.<n>We propose DRIVE, a novel SFUDA framework leveraging a dual-model architecture. The two models, with identical weights, work in parallel to capture diverse target domain characteristics.
  arXiv  Detail & Related papers  (2024-11-24T20:35:04Z)
• OMLog: Online Log Anomaly Detection for Evolving System with Meta-learning [10.181157278476428]
  OMLog is a real-time and reliable online log anomaly detection model. We introduce a maximum mean discrepancy-based distribution shift detection method. We also design an online learning mechanism based on meta-learning, which can effectively learn the highly repetitive patterns of log sequences.
  arXiv  Detail & Related papers  (2024-10-22T01:50:07Z)
• Fault Detection and Monitoring using a Data-Driven Information-Based Strategy: Method, Theory, and Application [5.056456697289351]
  We propose an information-driven fault detection method based on a novel concept drift detector.<n>The method is tailored to identifying drifts in input-output relationships of additive noise models.<n>We prove several theoretical properties of the proposed MI-based fault detection scheme.
  arXiv  Detail & Related papers  (2024-05-06T17:43:39Z)
• MAPS: A Noise-Robust Progressive Learning Approach for Source-Free Domain Adaptive Keypoint Detection [76.97324120775475]
  Cross-domain keypoint detection methods always require accessing the source data during adaptation. This paper considers source-free domain adaptive keypoint detection, where only the well-trained source model is provided to the target domain.
  arXiv  Detail & Related papers  (2023-02-09T12:06:08Z)
• Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning [122.62311703151215]
  Divide and Contrast (DaC) aims to connect the good ends of both worlds while bypassing their limitations. DaC divides the target data into source-like and target-specific samples, where either group of samples is treated with tailored goals. We further align the source-like domain with the target-specific samples using a memory bank-based Maximum Mean Discrepancy (MMD) loss to reduce the distribution mismatch.
  arXiv  Detail & Related papers  (2022-11-12T09:21:49Z)
• Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection [85.11649974840758]
  3D object detection networks tend to be biased towards the data they are trained on. We propose a single-frame approach for source-free, unsupervised domain adaptation of lidar-based 3D object detectors.
  arXiv  Detail & Related papers  (2021-11-30T18:42:42Z)
• TraND: Transferable Neighborhood Discovery for Unsupervised Cross-domain Gait Recognition [77.77786072373942]
  This paper proposes a Transferable Neighborhood Discovery (TraND) framework to bridge the domain gap for unsupervised cross-domain gait recognition. We design an end-to-end trainable approach to automatically discover the confident neighborhoods of unlabeled samples in the latent space. Our method achieves state-of-the-art results on two public datasets, i.e., CASIA-B and OU-LP.
  arXiv  Detail & Related papers  (2021-02-09T03:07:07Z)
• Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation [102.67010690592011]
  Unsupervised adaptationUDA (UDA) aims to leverage the knowledge learned from a labeled source dataset to solve similar tasks in a new unlabeled domain. Prior UDA methods typically require to access the source data when learning to adapt the model. This work tackles a practical setting where only a trained source model is available and how we can effectively utilize such a model without source data to solve UDA problems.
  arXiv  Detail & Related papers  (2020-02-20T03:13:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.