Work

GN5-2 Anomaly Detection in perfSONAR Data Using Autoencoders

Year: 2026

Type: article

Abstract: This paper presents an approach to anomaly detection in network performance data collected through the perfSONAR-based Performance Measurement Platform (PMP), in GN5-2 Work Package 6, Task 3. Using unsupervised machine learning techniques, specifically autoencoders, the study focuses on identifying deviations in latency distribution without relying on labelled datasets. Two approaches are evaluated: direct histogram-based learning and statistical feature extraction. Results demonstrate that the first proposed method successfully detects both transient anomalies and sustained distribution shifts, highlighting its potential for real-time network monitoring.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.21496464

Authors: Hrboka, Ljubomir, Šodan, Krešimir

Institution: Croatian Academic and Research Network

Language: English

Topic: Software System Performance and Reliability

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

Funder: European Commission