Active Rule Mining for Multivariate Anomaly Detection in Radio Access Networks

TL;DR

This paper introduces a semi-autonomous rule mining approach for multivariate anomaly detection in radio access networks, providing interpretable rules to help network operators understand and respond to anomalies effectively.

Contribution

It proposes a novel rule mining method tailored for RAN anomaly detection that enhances interpretability of multivariate anomalies in both discrete and time series data.

Findings

Effective interpretation of anomalies in RAN data

Applicable to both discrete and time series data

Improves understanding of anomalies for network management

Abstract

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.