提出可自动生成可解释规则的异常检测系统，助力网络运维定位问题根源。
Active Rule Mining for Multivariate Anomaly Detection in Radio Access Networks
- 基于半自动方法挖掘多变量异常的通用可解释规则
- 在无线接入网时序数据上验证，能识别业务层面真正异常的模式
- 适合网络工程师用于快速定位故障原因并制定修复策略
多变量异常检测在诸多应用中具有重要意义。尽管已有多种检测器，但难以解释检测到的异常为何异常，这对网络运营商理解异常根本原因及采取应对措施至关重要。现有可解释AI方法仅能提示影响异常的特征，无法生成领域专家可评估的通用规则。此外，并非所有离群点在业务层面都算异常。当前亟需一种能解释多变量异常检测结果并将其映射为可操作规则的系统。本文提出一种半自治异常规则挖掘方法，适用于离散与时间序列数据，特别针对无线接入网（RAN）异常检测场景。实验在时序RAN数据上验证了该方法的有效性。
原文摘要 · Abstract (English)
Multivariate anomaly detection finds its importance in diverse applications. Despite the existence of many detectors to solve this problem, one cannot simply define why an obtained anomaly inferred by the detector is anomalous. This reasoning is required for network operators to understand the root cause of the anomaly and the remedial action that should be taken to counteract its occurrence. Existing solutions in explainable AI may give cues to features that influence an anomaly, but they do not formulate generalizable rules that can be assessed by a domain expert. Furthermore, not all outliers are anomalous in a business sense. There is an unfulfilled need for a system that can interpret anomalies predicted by a multivariate anomaly detector and map these patterns to actionable rules. This paper aims to fulfill this need by proposing a semi-autonomous anomaly rule miner. The proposed method is applicable to both discrete and time series data and is tailored for radio access network (RAN) anomaly detection use cases. The proposed method is demonstrated in this paper with time series RAN data.
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