Analyzing Hack Subnetworks in the Bitcoin Transaction Graph

Goldsmith, Daniel; Grauer, Kim; Shmalo, Yonah

doi:10.48550/arxiv.1910.13415

Cited by 2 publications

(4 citation statements)

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“…We used data pre-processed by Chainalysis following the approach detailed in [22][23][24]. The preprocessing relies on state-of-the-art heuristics [18][19][20][21]27], including co-spending clustering, intelligencebased clustering, behavioural clustering and entity identification through direct interaction [23].…”

Section: Methodsmentioning

confidence: 99%

“…Here, we investigate the dynamics of 24 market closures by looking at 31 markets in the period between June 2011 to July 2019. We do so by investigating a novel dataset of Bitcoin transactions involving dark markets assembled on the basis of the most recent identification methods [22][23][24]. For the first time, we quantify the overall activity of the major dark markets, in terms of number of users and total volume traded.…”

Section: Introductionmentioning

confidence: 99%

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Collective dynamics of dark web marketplaces

ElBahrawy

Alessandretti

Rusnac³

et al. 2020

Sci Rep

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Dark web marketplaces are websites that facilitate trade in illicit goods, mainly using Bitcoin. Since dark web marketplaces are unregulated, they do not offer any user protection, so police raids and scams regularly cause large losses to marketplace participants. However, the uncertainty has not prevented the proliferation of dark web marketplaces. Here, we investigate how the dark web marketplace ecosystem reorganises itself following marketplace closures. We analyse 24 separate episodes of unexpected marketplace closure by inspecting 133 million Bitcoin transactions among 38 million users. We focus on “migrating users” who move their trading activity to a different marketplace after a closure. We find that most migrating users continue their trading activity on a single coexisting marketplace, typically the one with the highest trading volume. User migration is swift and trading volumes of migrating users recover quickly. Thus, although individual marketplaces might appear fragile, coordinated user migration guarantees overall systemic resilience.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Collective dynamics of dark web marketplaces

ElBahrawy

Alessandretti

Rusnac³

et al. 2020

Sci Rep

Self Cite

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“…Ref. [19] studied how different features of the date influence communities results on the "transaction graph" based on the ground truth of some known hack subnetworks. Authors in Ref.…”

Section: A Related Workmentioning

confidence: 99%

“…weight between any two nodes could be defined following criterion in Refs. [19,22,43]. Herein, we extract features from the total transaction amount and set them as the edge weight.…”

Section: B Community Detection For Bitcoinmentioning

confidence: 99%

Community Detection in Blockchain Social Networks

Wu¹,

Wu²,

Chen³

et al. 2021

Preprint

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In this work, we consider community detection in blockchain networks. We specifically take the Bitcoin network and Ethereum network as two examples, where community detection serves in different ways. For the Bitcoin network, we modify the traditional community detection method and apply it to the transaction social network to cluster users with similar characteristics. For the Ethereum network, on the other hand, we define a bipartite social graph based on the smart contract transactions. A novel community detection algorithm which is designed for low-rank signals on graph can help find users' communities based on user-token subscription. Based on these results, two strategies are devised to deliver onchain advertisements to those users in the same community. We implement the proposed algorithms on real data. By adopting the modified clustering algorithm, the community results in the Bitcoin network is basically consistent with the ground-truth of betting site community which has been announced to the public. At the meanwhile, we run the proposed strategy on real Ethereum data, visualize the results and implement an advertisement delivery on the Ropsten test net.

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Analyzing Hack Subnetworks in the Bitcoin Transaction Graph

Cited by 2 publications

References 0 publications

Collective dynamics of dark web marketplaces

Collective dynamics of dark web marketplaces

Community Detection in Blockchain Social Networks

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