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
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.File in questo prodotto:
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