Active Rule Mining for Multivariate Anomaly Detection in Radio Access Networks

2025-01-11 · Ebenezer R. H. P. Isaac, Joseph H. R. Isaac

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.

📄 PDF Abstract BibTeX arXiv:2501.06571

Code (0)

등록된 구현이 없습니다.

Tasks

Anomaly DetectionTime Series

RoLA: A Real-Time Online Lightweight Anomaly Detection System for Multivariate Time Series

2023-05-25 · Ming-Chang Lee, Jia-Chun Lin

A multivariate time series refers to observations of two or more variables taken from a device or a system simultaneously over time. There is an increasing need to monitor multivariate time series and detect anomalies in…

Anomaly Detection Time Series Time Series Anomaly Detection

From Explanation to Action: An End-to-End Human-in-the-loop Framework for Anomaly Reasoning and Management

2023-04-06 · Xueying Ding, Nikita Seleznev, Senthil Kumar, C. Bayan Bruss 외

Anomalies are often indicators of malfunction or inefficiency in various systems such as manufacturing, healthcare, finance, surveillance, to name a few. While the literature is abundant in effective detection algorithms…

Anomaly Detection Management

GenAD: General Representations of Multivariate Time Series for Anomaly Detection

2021-01-01 · Xiaolei Hua, Su Wang, Lin Zhu, Dong Zhou 외

Anomaly Detection(AD) for multivariate time series is an active area in machine learning, with critical applications in Information Technology system management, Spacecraft Health monitoring, Multi-Robot Systems detectio…

Anomaly Detection Management Time Series Time Series Analysis+1

CANDI: Curated Test-Time Adaptation for Multivariate Time-Series Anomaly Detection Under Distribution Shift

2026-04-02 · HyunGi Kim, Jisoo Mok, Hyungyu Lee, Juhyeon Shin 외 arxiv

Multivariate time-series anomaly detection (MTSAD) aims to identify deviations from normality in multivariate time-series and is critical in real-world applications. However, in real-world deployments, distribution shift…

Test-time Adaptation Anomaly Detection

Deep Federated Anomaly Detection for Multivariate Time Series Data

2022-05-09 · Wei Zhu, Dongjin Song, Yuncong Chen, Wei Cheng 외

Despite the fact that many anomaly detection approaches have been developed for multivariate time series data, limited effort has been made on federated settings in which multivariate time series data are heterogeneously…

Anomaly Detection Constrained Clustering Federated Learning Time Series+2