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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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Engineering Science and Technology, an International Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215098625001740 |
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