用ASTD语言构建可连续学习的日志异常检测系统
ASTD Patterns for Integrated Continuous Anomaly Detection In Data Logs
- 基于滑动窗口实现数据流的持续学习与模型更新
- 提出新操作符Quantified Flow，支持模型无缝融合
- 适合需要模块化设计的实时日志监控系统开发者
本文研究了在数据日志中使用ASTD语言进行集成异常检测。采用滑动窗口技术实现数据流的持续学习，并在每个窗口结束时更新学习模型，以保持检测准确性并紧跟当前数据趋势。针对无监督学习场景，提出ASTD模式来组合多个学习模型。为此，引入一种新的ASTD操作符——量化解析流（Quantified Flow），可在保证规格简洁的同时实现学习模型的无缝结合。本文贡献在于提出一种规格化模式，凸显了ASTD在抽象和模块化异常检测系统方面的潜力。ASTD语言通过图形化操作符组合过程，为开发数据流异常检测系统提供独特方法，简化了设计任务，使开发者能专注于定义系统功能操作。
原文摘要 · Abstract (English)
This paper investigates the use of the ASTD language for ensemble anomaly detection in data logs. It uses a sliding window technique for continuous learning in data streams, coupled with updating learning models upon the completion of each window to maintain accurate detection and align with current data trends. It proposes ASTD patterns for combining learning models, especially in the context of unsupervised learning, which is commonly used for data streams. To facilitate this, a new ASTD operator is proposed, the Quantified Flow, which enables the seamless combination of learning models while ensuring that the specification remains concise. Our contribution is a specification pattern, highlighting the capacity of ASTDs to abstract and modularize anomaly detection systems. The ASTD language provides a unique approach to develop data flow anomaly detection systems, grounded in the combination of processes through the graphical representation of the language operators. This simplifies the design task for developers, who can focus primarily on defining the functional operations that constitute the system.
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