面向无线接入网中多变量异常检测的主动规则挖掘
Active Rule Mining for Multivariate Anomaly Detection in Radio Access Networks
AI总结：
针对多变量异常检测缺乏可解释、可落地的业务规则的问题，本文提出一种适配无线接入网场景、支持离散和时间序列数据的半自治异常规则挖掘器，可将异常模式映射为可执行规则。
摘要：
多变量异常检测在各类应用中均具有重要价值。尽管已有众多检测器可解决该问题，但人们无法直接明确检测器推断出的异常之所以为异常的原因。网络运维人员需要这类推理信息来理解异常的根本原因，以及为遏制异常发生应采取的补救措施。现有可解释人工智能方案或许能给出影响异常的特征线索，但无法形成可供领域专家评估的可泛化规则。此外，并非所有离群点在业务层面都属于异常。目前亟需一套能够解释多变量异常检测器预测的异常、并将这些模式映射为可执行规则的系统，这一需求尚未得到满足。本文旨在通过提出一种半自治异常规则挖掘器来填补该空白。所提方法既适用于离散数据也适用于时间序列数据，是针对无线接入网（RAN）异常检测用例定制的。本文利用RAN时间序列数据对所提方法进行了验证。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
