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  • May 2022
  • Article

Coins for Bombs: The Predictive Ability of On-Chain Transfers for Terrorist Attacks

By: Dan Amiram, Evgeny Lyandres and Daniel Rabetti
This study examines whether we can learn from the behavior of blockchain-based transfers to predict the financing of terrorist attacks. We exploit blockchain transaction transparency to map millions of transfers for hundreds of large on-chain service providers. The... View Details
Keywords: Blockchain; Bitcoin; Accounting; AI and Machine Learning; National Security; Governing Rules, Regulations, and Reforms
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  • 2016
  • Article

Penalized Fast Subset Scanning

By: Skyler Speakman, Sriram Somanchi, Edward McFowland III and Daniel B. Neill
We present the penalized fast subset scan (PFSS), a new and general framework for scalable and accurate pattern detection. PFSS enables exact and efficient identification of the most anomalous subsets of the data, as measured by a likelihood ratio scan statistic.... View Details
Keywords: Disease Surveillance; Likelihood Ratio Statistic; Pattern Detection; Scan Statistic; Mathematical Methods
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  • 2015
  • Article

Scalable Detection of Anomalous Patterns With Connectivity Constraints

By: Skyler Speakman, Edward McFowland III and Daniel B. Neill
We present GraphScan, a novel method for detecting arbitrarily shaped connected clusters in graph or network data. Given a graph structure, data observed at each node, and a score function defining the anomalousness of a set of nodes, GraphScan can efficiently and... View Details
Keywords: Biosurveillance; Event Detection; Graph Mining; Scan Statistics; Spatial Scan Statistic
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  • Article

Fast Subset Scan for Multivariate Spatial Biosurveillance

By: Daniel B. Neill, Edward McFowland III and Huanian Zheng
We present new subset scan methods for multivariate event detection in massive space-time datasets. We extend the recently proposed 'fast subset scan' framework from univariate to multivariate data, enabling computationally efficient detection of irregular space-time... View Details
Keywords: Algorithms; Disease Surveillance; Event Detection; Scan Statistics; Spatial Scan
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  • 2011
  • Article

Scalable Detection of Anomalous Patterns With Connectivity Constraints

By: Skyler Speakman, Edward McFowland III and Daniel B. Neill
We present GraphScan, a novel method for detecting arbitrarily shaped connected clusters in graph or network data. Given a graph structure, data observed at each node, and a score function defining the anomalousness of a set of nodes, GraphScan can efficiently and... View Details
Keywords: Biosurveillance; Event Detection; Graph Mining; Scan Statistics; Spatial Scan Statistic
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