Anomaly Detection on Event Logs with a Scarcity of Labels | IEEE Conference Publication | IEEE Xplore

Anomaly Detection on Event Logs with a Scarcity of Labels


Abstract:

Assuring anomaly-free business process executions is a key challenge for many organizations. Traditional techniques address this challenge using prior knowledge about ano...Show More

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.
Date of Conference: 05-08 October 2020
Date Added to IEEE Xplore: 22 October 2020
ISBN Information:
Conference Location: Padua, Italy

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