《不稳定日志数据的鲁棒性缺陷检测模型研究》

Robust Log-Based Anomaly Detection on Unstable Log Data is a research paper that delves into the challenges of detecting anomalies in log data from large and complex software-intensive systems. The paper, originally titled "不稳定日志数据的基于日志的鲁棒性缺陷检测," discusses the limitations of existing methods and proposes a new approach to address these limitations. Logs are an essential tool for troubleshooting in software-intensive systems, providing valuable insights into system behavior and performance. However, the unstable nature of log data poses a significant challenge for anomaly detection. The paper argues that existing methods for anomaly detection rely on a closed-world assumption, which limits their effectiveness in practical applications. To address these limitations, the paper introduces a new approach to log-based anomaly detection that is robust to the unstable nature of log data. The proposed method involves constructing a detection model using log event data extracted from historical logs. Unlike existing methods, the new approach does not rely on the closed-world assumption, making it more suitable for practical applications. The paper presents experimental results to demonstrate the effectiveness of the proposed method. The results show that the new approach outperforms existing methods in detecting anomalies in unstable log data. Additionally, the paper discusses the implications of the findings and provides recommendations for future research in this area. In summary, Robust Log-Based Anomaly Detection on Unstable Log Data addresses the limitations of existing methods for log-based anomaly detection and proposes a new approach that is robust to the unstable nature of log data. The paper provides valuable insights for researchers and practitioners working in the field of software-intensive systems and lays the groundwork for future advancements in log-based anomaly detection.