Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Applied Intelligence and Informatics |
| Untertitel | Second International Conference, AII 2022, Reggio Calabria, Italy, September 1–3, 2022, Proceedings |
| Herausgeber/-innen | Mufti Mahmud, Cosimo Ieracitano, Nadia Mammone, Francesco Carlo Morabito, M. Shamim Kaiser |
| Herausgeber (Verlag) | Springer Science and Business Media Deutschland GmbH |
| Seiten | 511-522 |
| Seitenumfang | 12 |
| ISBN (elektronisch) | 978-3-031-24801-6 |
| ISBN (Print) | 9783031248009 |
| Publikationsstatus | Veröffentlicht - 2 Feb. 2023 |
| Veranstaltung | 2nd International Conference on Applied Intelligence and Informatics, AII 2022 - Reggio Calabria, Italien Dauer: 1 Sept. 2022 → 3 Sept. 2022 |
Publikationsreihe
| Name | Communications in Computer and Information Science |
|---|---|
| Band | 1724 CCIS |
| ISSN (Print) | 1865-0929 |
| ISSN (elektronisch) | 1865-0937 |
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.
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Applied Intelligence and Informatics: Second International Conference, AII 2022, Reggio Calabria, Italy, September 1–3, 2022, Proceedings. Hrsg. / Mufti Mahmud; Cosimo Ieracitano; Nadia Mammone; Francesco Carlo Morabito; M. Shamim Kaiser. Springer Science and Business Media Deutschland GmbH, 2023. S. 511-522 (Communications in Computer and Information Science; Band 1724 CCIS).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - A Graph-Based Approach to Detect Anomalies Based on Shared Attribute Values
AU - Brauer, Steffen
AU - Fisichella, Marco
AU - Lax, Gianluca
AU - Romeo, Carlo
AU - Russo, Antonia
N1 - Funding Information: Acknowledgements. This work was partially supported by “iCARE” project (CUP J39J14001400007) - action 10.5.12 - funded within POR FESR FSE 2014/2020 of Calabria Region with the participation of European Community Resources of FESR and FSE, of Italy and of Calabria.
PY - 2023/2/2
Y1 - 2023/2/2
N2 - 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.
AB - 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.
KW - Anomaly detection
KW - Fraud detection
KW - Outlier detection
UR - http://www.scopus.com/inward/record.url?scp=85149692967&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-24801-6_36
DO - 10.1007/978-3-031-24801-6_36
M3 - Conference contribution
AN - SCOPUS:85149692967
SN - 9783031248009
T3 - Communications in Computer and Information Science
SP - 511
EP - 522
BT - Applied Intelligence and Informatics
A2 - Mahmud, Mufti
A2 - Ieracitano, Cosimo
A2 - Mammone, Nadia
A2 - Morabito, Francesco Carlo
A2 - Kaiser, M. Shamim
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd International Conference on Applied Intelligence and Informatics, AII 2022
Y2 - 1 September 2022 through 3 September 2022
ER -