【摘要】 本文综述了日志解析和异常检测技术的研究进展与未来发展方向。在当今数字化时代，系统日志异常检测已成为系统运维的重要工作之一。随着系统功能拓展，其内部结构也愈加复杂，系统日志的数据量也随之激增，给传统的日志解析与异常检测方法带来了巨大挑战。本文首先总结了近年来日志解析与异常检测领域的研究成果，重点介绍了深度学习技术在该领域中的应用优势与发展趋势。接着，本文分析了当前日志解析与异常检测面临的主要问题，包括公开数据集匮乏、评估指标不统一以及缺乏在实际生产环境中的性能验证等。最后，本文探讨了未来的研究方向，如利用迁移学习缓解数据不足问题，提升异常检测模型的泛化能力与实际适用性。更多还原

【Abstract】 This paper reviews recent advances and future directions in log parsing and anomaly detection. In today’s digital era, system log anomaly detection has become a critical task in system operation and maintenance. As system functionalities expand and internal architectures grow increasingly complex, the volume of system logs has grown exponentially, posing significant challenges to traditional log parsing and anomaly detection methods. We first summarize recent research achievements in this field, with an emphasis on the advantages and emerging trends of deep learning approaches. Then, we analyze key challenges, including the scarcity of publicly available datasets, inconsistent evaluation metrics, and insufficient validation in real-world production environments. Finally, we discuss promising future research directions, such as leveraging transfer learning to mitigate data scarcity and enhance model generalization and practical applicability.更多还原