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001-es BibID:	BIBFORM125218
035-os BibID:	(Scopus)85205756822
Első szerző:	Vajda Dániel László
Cím:	Machine learning-based real-time anomaly detection using data pre-processing in the telemetry of server farms / Dániel László Vajda, Tien Van Do, Tamás Bérczes, Károly Farkas
Dátum:	2024
ISSN:	2045-2322
Megjegyzések:	Fast and accurate anomaly detection is critical in telemetry systems because it helps operators take appropriate actions in response to abnormal behaviours. However, recent techniques are accurate but not fast enough to deal with real-time data. There is a need to reduce the anomaly detection time, which motivates us to propose two new algorithms called AnDePeD (Anomaly Detector on Periodic Data) and AnDePed Pro. The novelty of the proposed algorithms lies in exploiting the periodic nature of data in anomaly detection. Our proposed algorithms apply a variational mode decomposition technique to find and extract periodic components from the original data before using Long ShortTerm Memory neural networks to detect anomalies in the remainder time series. Furthermore, our methods include advanced techniques to eliminate prediction errors and automatically tune operational parameters. Extensive numerical results show that the proposed algorithms achieve comparable performance in terms of Precision, Recall, F-score, and MCC metrics while outperforming most of the state-of-the-art anomaly detection approaches in terms of initialisation delay and detection delay, which is favourable for practical applications.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Server farm telemetry Real-time anomaly detection Periodic data Pre-processing AnDePeD AnDePeD Pro VMD Mode removal
Megjelenés:	Scientific Reports. - 14 : 1 (2024), p. 1-22. -
További szerzők:	Do, Tien Van Bérczes Tamás (1975-) (informatikus) Farkas Károly (1975-) (informatikus)
Pályázati támogatás:	K-138208 OTKA
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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