Search Results for author:
Found 5 papers, 2 papers with code
GuiltyWalker: Distance to illicit nodes in the Bitcoin network
Money laundering is a global phenomenon with wide-reaching social and economic consequences.
TripMD: Driving patterns investigation via Motif Analysis
Processing driving data and investigating driving behavior has been receiving an increasing interest in the last decades, with applications ranging from car insurance pricing to policy making.
Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity
First, we show that existing state-of-the-art solutions using unsupervised anomaly detection methods are inadequate to detect the illicit patterns in a real Bitcoin transaction dataset.
Exploring time-series motifs through DTW-SOM
In this paper, we argue that visually exploring time-series motifs computed by motif discovery algorithms can be useful to understand and debug results.
