Revealing anomalies in data usually suggests significant - also critical - actionable information in a wide variety of application domains. Anomaly detection can support dependability monitoring when traditional detection mechanisms e.g., based on event logs, probes and heartbeats, are considered inadequate or not applicable. On the other hand, checking the behavior of complex and dynamic system it is not trivial, since the notion of “normal” – and, consequently, anomalous - behavior is changing frequently according to the characteristics of such system. In such a context, performing anomaly detection calls for dedicate strategies and techniques that are not consolidated in the state-of-the-art. The paper expands the context, the challenges and the work done so far in association with our current research direction. The aim is to highlight the challenges and the future works that the PhD student tackled and will tackle in the next years.

Executing Online Anomaly Detection in Complex Dynamic Systems / Zoppi, Tommaso. - ELETTRONICO. - (2017), pp. 86-89. (Intervento presentato al convegno 24th PhD MiniSymposium tenutosi a Budapest nel January, 30th-31st, 2017) [10.5281/zenodo.291908].

Executing Online Anomaly Detection in Complex Dynamic Systems

ZOPPI, TOMMASO
2017

Abstract

Revealing anomalies in data usually suggests significant - also critical - actionable information in a wide variety of application domains. Anomaly detection can support dependability monitoring when traditional detection mechanisms e.g., based on event logs, probes and heartbeats, are considered inadequate or not applicable. On the other hand, checking the behavior of complex and dynamic system it is not trivial, since the notion of “normal” – and, consequently, anomalous - behavior is changing frequently according to the characteristics of such system. In such a context, performing anomaly detection calls for dedicate strategies and techniques that are not consolidated in the state-of-the-art. The paper expands the context, the challenges and the work done so far in association with our current research direction. The aim is to highlight the challenges and the future works that the PhD student tackled and will tackle in the next years.
2017
24-th PhD MiniSymposium
24th PhD MiniSymposium
Budapest
January, 30th-31st, 2017
Zoppi, Tommaso
File in questo prodotto:
File Dimensione Formato  
PhD_Minisymp_AnomalyDetection_V2_CameraReady.pdf

accesso aperto

Descrizione: CameraReady
Tipologia: Pdf editoriale (Version of record)
Licenza: Creative commons
Dimensione 756.04 kB
Formato Adobe PDF
756.04 kB Adobe PDF

I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1073176
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact