Anomaly detection is an important task in many fields such as eHealth and online fraud. In this paper, we propose a new technique for anomaly detection based on a graph that connects transactions with the same attribute values and searches for dense clusters indicative of an anomalous pattern. The experimental evaluation shows that the graph-based approach outperforms two other approaches in the considered dataset. The extension of this approach to the eHealth domain is reserved as future work.
A Graph-Based Approach to Detect Anomalies Based on Shared Attribute Values / Brauer, S., Fisichella, M., Lax, G., Romeo, C., Russo, A.. - 1724:(2022), pp. 511-522. [10.1007/978-3-031-24801-6_36]
A Graph-Based Approach to Detect Anomalies Based on Shared Attribute Values
Lax G.
;Russo Antonia
2022-01-01
Abstract
Anomaly detection is an important task in many fields such as eHealth and online fraud. In this paper, we propose a new technique for anomaly detection based on a graph that connects transactions with the same attribute values and searches for dense clusters indicative of an anomalous pattern. The experimental evaluation shows that the graph-based approach outperforms two other approaches in the considered dataset. The extension of this approach to the eHealth domain is reserved as future work.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
