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Detecting Illicit Entities in Bitcoin using Supervised Learning of Ensemble Decision Trees

Pranav Nerurkar 1 Yann Busnel 2, 3 Romaric Ludinard 2, 4 Kunjal Shah 1 Sunil Bhirud 1 Dhiren Patel 1
3 DIONYSOS - Dependability Interoperability and perfOrmance aNalYsiS Of networkS
Inria Rennes – Bretagne Atlantique , IRISA-D2 - RÉSEAUX, TÉLÉCOMMUNICATION ET SERVICES
4 ADOPNET - Advanced technologies for operated networks
UR1 - Université de Rennes 1, IMT Atlantique - IMT Atlantique Bretagne-Pays de la Loire, IRISA-D2 - RÉSEAUX, TÉLÉCOMMUNICATION ET SERVICES
Abstract : Since its inception in 2009, Bitcoin has been mired in controversies for providing a haven for illegal activities. Several types of illicit users hide behind the blanket of anonymity. Uncovering these entities is key for forensic investigations. Current methods utilize machine learning for identifying these illicit entities. However, the existing approaches only focus on a limited category of illicit users. The current paper proposes to address the issue by implementing an ensemble of decision trees for supervised learning. More parameters allow the ensemble model to learn discriminating features that can categorize multiple groups of illicit users from licit users. To evaluate the model, a dataset of 2059 real-life entities on Bitcoin was extracted from the Blockchain. Nine features were engineered to train the model for segregating 28 different licit-illicit categories of users. The proposed model provided a reliable tool for forensic study. Empirical evaluation of the proposed model vis-a-vis three existing benchmark models was performed to highlight its efficacy. Experiments showed that the specificity and sensitivity of the proposed model were comparable to other models.
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https://hal-imt-atlantique.archives-ouvertes.fr/hal-02952081
Contributor : Yann Busnel <>
Submitted on : Friday, November 27, 2020 - 10:22:29 AM
Last modification on : Thursday, January 7, 2021 - 4:20:26 PM

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Pranav Nerurkar, Yann Busnel, Romaric Ludinard, Kunjal Shah, Sunil Bhirud, et al.. Detecting Illicit Entities in Bitcoin using Supervised Learning of Ensemble Decision Trees. ICICM 2020 : 10th International Conference on Information Communication and Management, Aug 2020, Paris, France. pp.25-30, ⟨10.1145/3418981.3418984⟩. ⟨hal-02952081⟩

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