Cross-System Software Log-based Anomaly Detection Using Meta-Learning

• URL: http://arxiv.org/abs/2412.15445v2
• Date: Sat, 25 Jan 2025 21:31:54 GMT
• Title: Cross-System Software Log-based Anomaly Detection Using Meta-Learning
• Authors: Yuqing Wang, Mika V. Mäntylä, Jesse Nyyssölä, Ke Ping, Liqiang Wang,
• Abstract summary: AIOps tools have been developed to automate the process of log-based anomaly detection for software systems. Three practical challenges are widely recognized in this field: high data labeling costs, evolving logs in dynamic systems, and adaptability across different systems. We propose CroSysLog, an AIOps tool for log-event level anomaly detection, specifically designed in response to these challenges.
• Score: 17.39262430769509
• License:
• Abstract: Modern software systems produce vast amounts of logs, serving as an essential resource for anomaly detection. Artificial Intelligence for IT Operations (AIOps) tools have been developed to automate the process of log-based anomaly detection for software systems. Three practical challenges are widely recognized in this field: high data labeling costs, evolving logs in dynamic systems, and adaptability across different systems. In this paper, we propose CroSysLog, an AIOps tool for log-event level anomaly detection, specifically designed in response to these challenges. Following prior approaches, CroSysLog uses a neural representation approach to gain a nuanced understanding of logs and generate representations for individual log events accordingly. CroSysLog can be trained on source systems with sufficient labeled logs from open datasets to achieve robustness, and then efficiently adapt to target systems with a few labeled log events for effective anomaly detection. We evaluate CroSysLog using open datasets of four large-scale distributed supercomputing systems: BGL, Thunderbird, Liberty, and Spirit. We used random log splits, maintaining the chronological order of consecutive log events, from these systems to train and evaluate CroSysLog. These splits were widely distributed across a one/two-year span of each system's log collection duration, thereby capturing the evolving nature of the logs in each system. Our results show that, after training CroSysLog on Liberty and BGL as source systems, CroSysLog can efficiently adapt to target systems Thunderbird and Spirit using a few labeled log events from each target system, effectively performing anomaly detection for these target systems. The results demonstrate that CroSysLog is a practical, scalable, and adaptable tool for log-event level anomaly detection in operational and maintenance contexts of software systems.

Related papers

• Log2graphs: An Unsupervised Framework for Log Anomaly Detection with Efficient Feature Extraction [1.474723404975345]
  High cost of manual annotation and dynamic nature of usage scenarios present major challenges to effective log analysis. This study proposes a novel log feature extraction model called DualGCN-LogAE, designed to adapt to various scenarios. We also introduce Log2graphs, an unsupervised log anomaly detection method based on the feature extractor.
  arXiv  Detail & Related papers  (2024-09-18T11:35:58Z)
• LogFormer: A Pre-train and Tuning Pipeline for Log Anomaly Detection [73.69399219776315]
  We propose a unified Transformer-based framework for Log anomaly detection (LogFormer) to improve the generalization ability across different domains. Specifically, our model is first pre-trained on the source domain to obtain shared semantic knowledge of log data. Then, we transfer such knowledge to the target domain via shared parameters.
  arXiv  Detail & Related papers  (2024-01-09T12:55:21Z)
• RAPID: Training-free Retrieval-based Log Anomaly Detection with PLM considering Token-level information [7.861095039299132]
  The need for log anomaly detection is growing, especially in real-world applications. Traditional deep learning-based anomaly detection models require dataset-specific training, leading to corresponding delays. We introduce RAPID, a model that capitalizes on the inherent features of log data to enable anomaly detection without training delays.
  arXiv  Detail & Related papers  (2023-11-09T06:11:44Z)
• GLAD: Content-aware Dynamic Graphs For Log Anomaly Detection [49.9884374409624]
  GLAD is a Graph-based Log Anomaly Detection framework designed to detect anomalies in system logs. We introduce GLAD, a Graph-based Log Anomaly Detection framework designed to detect anomalies in system logs.
  arXiv  Detail & Related papers  (2023-09-12T04:21:30Z)
• AutoLog: A Log Sequence Synthesis Framework for Anomaly Detection [34.91789047641838]
  AutoLog is the first automated log generation methodology for anomaly detection. It generates run-time log sequences without actually running the system. It propagates the anomaly label to each acquired execution path based on human knowledge.
  arXiv  Detail & Related papers  (2023-08-18T05:56:18Z)
• LogGD:Detecting Anomalies from System Logs by Graph Neural Networks [14.813971618949068]
  We propose a novel graph-based log anomaly detection method, LogGD, to effectively address the issue. We exploit the powerful capability of Graph Transformer Neural Network, which combines graph structure and node semantics for log-based anomaly detection.
  arXiv  Detail & Related papers  (2022-09-16T11:51:58Z)
• Leveraging Log Instructions in Log-based Anomaly Detection [0.5949779668853554]
  We propose a method for reliable and practical anomaly detection from system logs. It overcomes the common disadvantage of related works by building an anomaly detection model with log instructions from the source code of 1000+ GitHub projects. The proposed method, named ADLILog, combines the log instructions and the data from the system of interest (target system) to learn a deep neural network model.
  arXiv  Detail & Related papers  (2022-07-07T10:22:10Z)
• LogLAB: Attention-Based Labeling of Log Data Anomalies via Weak Supervision [63.08516384181491]
  We present LogLAB, a novel modeling approach for automated labeling of log messages without requiring manual work by experts. Our method relies on estimated failure time windows provided by monitoring systems to produce precise labeled datasets in retrospect. Our evaluation shows that LogLAB consistently outperforms nine benchmark approaches across three different datasets and maintains an F1-score of more than 0.98 even at large failure time windows.
  arXiv  Detail & Related papers  (2021-11-02T15:16:08Z)
• Robust and Transferable Anomaly Detection in Log Data using Pre-Trained Language Models [59.04636530383049]
  Anomalies or failures in large computer systems, such as the cloud, have an impact on a large number of users. We propose a framework for anomaly detection in log data, as a major troubleshooting source of system information.
  arXiv  Detail & Related papers  (2021-02-23T09:17:05Z)
• Self-Attentive Classification-Based Anomaly Detection in Unstructured Logs [59.04636530383049]
  We propose Logsy, a classification-based method to learn log representations. We show an average improvement of 0.25 in the F1 score, compared to the previous methods.
  arXiv  Detail & Related papers  (2020-08-21T07:26:55Z)
• Self-Supervised Log Parsing [59.04636530383049]
  Large-scale software systems generate massive volumes of semi-structured log records. Existing approaches rely on log-specifics or manual rule extraction. We propose NuLog that utilizes a self-supervised learning model and formulates the parsing task as masked language modeling.
  arXiv  Detail & Related papers  (2020-03-17T19:25:25Z)

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.