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.Pubblicazioni consigliate
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