Novel metrics and LSH algorithms for unsupervised, real-time anomaly detection in multi-aspect data streams

Given a vast online stream of transactions in e-markets, how can we detect fraudulent traders and suspicious behaviors in an unsupervised manner? Can we detect them in constant time and memory? Fraud detection in e-markets is increasingly challenging due to the scale and complexity of multi-aspect d...

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Veröffentlicht in:	Engineering science and technology, an international journal Jg. 69; S. 102119
Hauptverfasser:	Khodabandehlou, Samira, Hashemi Golpayegani, Alireza
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.09.2025 Elsevier
Schlagworte:	Locality-sensitive hashing Market manipulation detection Multi-aspect data Real-time anomaly detection Stream mining Unsupervised learning Real-time anomaly detection Stream mining Market manipulation detection Multi-aspect data Locality-sensitive hashing Unsupervised learning
ISSN:	2215-0986, 2215-0986
Online-Zugang:	Volltext
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