Proactive Network Maintenance using Fast, Accurate Anomaly Localization and Classification on 1-D Data Series

TL;DR

This paper presents a deep learning-based algorithm for proactive network maintenance that accurately localizes and classifies anomalies in 1-D data series, enabling early fault detection.

Contribution

It introduces a novel deep convolutional neural network approach for anomaly detection and classification in network data series, achieving high accuracy.

Findings

Achieved 97.82% mean average precision in anomaly detection

Demonstrated efficient and accurate localization of network faults

Enhanced proactive maintenance capabilities

Abstract

Proactive network maintenance (PNM) is the concept of using data from a network to identify and locate network faults, many or all of which could worsen to become service failures. The separation between the network fault and the service failure affords early detection of problems in the network to allow PNM to take place. Consequently, PNM is a form of prognostics and health management (PHM). The problem of localizing and classifying anomalies on 1-dimensional data series has been under research for years. We introduce a new algorithm that leverages Deep Convolutional Neural Networks to efficiently and accurately detect anomalies and events on data series, and it reaches 97.82% mean average precision (mAP) in our evaluation.