Identifying criminal organizations from their social network structures

Key Findings & Data:The empirical results show that these three methods can provide significant improvements in distinguishing all three network types.

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Conclusion & Impact:We show that these methods are viable enough to be used as supporting evidence by security forces in their fight against criminal organizations operating on social networks.

🔬Design:Modeling & Simulation

📍Scope:Comprehensive Sample

⚙️Variables:Criminal networks · identification · decision tree

Abstract

Identification of criminal structures within very large social networks is an essential security feat. By identifying such structures, it may be possible to track, neutralize, and terminate the corresponding criminal organizations before they act. We evaluate the effectiveness of three different methods for classifying an unknown network as terrorist, cocaine, or noncriminal. We consider three methods for the identification of network types: evaluating common social network analysis metrics, modeling with a decision tree, and network motif frequency analysis. The empirical results show that these three methods can provide significant improvements in distinguishing all three network types. We show that these methods are viable enough to be used as supporting evidence by security forces in their fight against criminal organizations operating on social networks.

Keywords

Criminal networks identification decision tree motif analysis machine learning

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Cite This Article

ÇINAR, M. S., GENÇ, B., SEVER, H. (2019). Identifying criminal organizations from their social network structures. Turkish Journal of Electrical Engineering and Computer Science, Cilt: 27 - Sayı: 1, 421–436. https://ajindex.com/articles/186806

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