Assuring anomaly-free business process executions is a key challenge for many organizations. Traditional techniques address this challenge using prior knowledge about anomalous cases that is seldom available in real-life. In this work, we propose the usage of word2vec encoding and One-Class Classification algorithms to detect anomalies by relying on normal behavior only. We investigated 6 different types of anomalies over 38 real and synthetics event logs, comparing the predictive performance of Support Vector Machine, One-Class Support Vector Machine, and Local Outlier Factor. Results show that our technique is viable for real-life scenarios, overcoming traditional machine learning for a wide variety of settings where only the normal behavior can be labeled.

Anomaly Detection on Event Logs with a Scarcity of Labels / S. Barbon Junior, P. Ceravolo, E. Damiani, N.J. Omori, G.M. Tavares - In: 2020 2nd International Conference on Process Mining (ICPM) / [a cura di] B. van Dongen, M. Montali, M. Thandar Wynn. - [s.l] : IEEE, 2020. - ISBN 9781728198323. - pp. 161-168 (( Intervento presentato al 2. convegno International Conference on Process Mining (ICPM) tenutosi a Padova nel 2020.

Anomaly Detection on Event Logs with a Scarcity of Labels

P. Ceravolo;E. Damiani;G.M. Tavares
2020

Abstract

Assuring anomaly-free business process executions is a key challenge for many organizations. Traditional techniques address this challenge using prior knowledge about anomalous cases that is seldom available in real-life. In this work, we propose the usage of word2vec encoding and One-Class Classification algorithms to detect anomalies by relying on normal behavior only. We investigated 6 different types of anomalies over 38 real and synthetics event logs, comparing the predictive performance of Support Vector Machine, One-Class Support Vector Machine, and Local Outlier Factor. Results show that our technique is viable for real-life scenarios, overcoming traditional machine learning for a wide variety of settings where only the normal behavior can be labeled.
One Class Classification; anomaly detection; Local Outlier Factor; encoding; Support Vector Machine
Settore INF/01 - Informatica
2020
Book Part (author)
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

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

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/780355
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 10
  • ???jsp.display-item.citation.isi??? 6
social impact