Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Cognitive Cascades within Media Ecosystems: Simulating Fragmentation, Selective Exposure and Media Tactics to Investigate Polarization Kapitel lesen Erstes Kapitel lesen

2023 | OriginalPaper | Buchkapitel

Pattern Analysis of Money Flows in the Bitcoin Blockchain

verfasst von : Natkamon Tovanich, Rémy Cazabet

Erschienen in: Complex Networks and Their Applications XI

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Bitcoin is the first and highest valued cryptocurrency that stores transactions in a publicly distributed ledger called the blockchain. Understanding the activity and behavior of Bitcoin actors is a crucial research topic as they are pseudonymous in the transaction network. In this article, we propose a method based on taint analysis to extract taint flows—dynamic networks representing the sequence of Bitcoins transferred from an initial source to other actors until dissolution. Then, we apply graph embedding methods to characterize taint flows. We evaluate our embedding method with taint flows from top mining pools and show that it can classify mining pools with high accuracy. We also found that taint flows from the same period show high similarity. Our work proves that tracing the money flows can be a promising approach to classifying source actors and characterizing different money flow patterns.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Graph Mining and Machine Learning for Shader Codes Analysis to Accelerate GPU Tuning
Nächstes Kapitel On the Empirical Association Between Trade Network Complexity and Global Gross Domestic Product
Literatur
1.
Ahmed, M., Shumailov, I., Anderson, R.: Tendrils of crime: visualizing the diffusion of stolen bitcoins. In: Graphical Models for Security, pp. 1–12. Springer (2019)
2.
Akcora, C.G., Li, Y., Gel, Y.R., Kantarcioglu, M.: BitcoinHeist: topological data analysis for ransomware prediction on the bitcoin blockchain. In: The 29th International Joint Conference on Artificial Intelligence, pp. 4439–4445. IJCAI (2020)
3.
Balthasar, T.D., Hernandez-Castro, J.: An analysis of bitcoin laundry services. In: Nordic Conference on Secure IT Systems, pp. 297–312. Springer (2017)
4.
Bartoletti, M., Lande, S., Loddo, A., Pompianu, L., Serusi, S.: Cryptocurrency scams: analysis and perspectives. IEEE Access 9, 148353–148373 (2021)CrossRef
5.
Bartoletti, M., Pes, B., Serusi, S.: Data mining for detecting bitcoin ponzi schemes. In: Crypto Valley Conference on Blockchain Technology, pp. 75–84. IEEE (2018)
6.
Cazabet, R., Baccour, R., Latapy, M.: Tracking bitcoin users activity using community detection on a network of weak signals. In: International Conference on Complex Networks and Their Applications, pp. 166–177. Springer (2017)
7.
8.
Di Battista, G., Di Donato, V., Patrignani, M., Pizzonia, M., Roselli, V., Tamassia, R.: Bitconeview: visualization of flows in the bitcoin transaction graph. In: IEEE Symposium on Visualization for Cyber Security, pp. 1–8. IEEE (2015)
9.
Ermilov, D., Panov, M., Yanovich, Y.: Automatic bitcoin address clustering. In: IEEE International Conference on Machine Learning and Applications, pp. 461–466. IEEE (2017)
10.
Goldfeder, S., Kalodner, H., Reisman, D., Narayanan, A.: When the cookie meets the blockchain: privacy risks of web payments via cryptocurrencies (2017). arXiv:​1708.​04748
11.
Harlev, M.A., Sun Yin, H., Langenheldt, K.C., Mukkamala, R., Vatrapu, R.: Breaking bad: de-anonymising entity types on the bitcoin blockchain using supervised machine learning. In: The 51st Hawaii International Conference on System Sciences. ScholarSpace/AIS Electronic Library (2018)
12.
Harrigan, M., Fretter, C.: The unreasonable effectiveness of address clustering. In: Internatinal IEEE Conferences on UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld, pp. 368–373. IEEE (2016)
13.
Ivanov, S., Burnaev, E.: Anonymous walk embeddings. In: International Conference on Machine Learning, pp. 2186–2195. PMLR (2018)
14.
15.
Jourdan, M., Blandin, S., Wynter, L., Deshpande, P.: Characterizing entities in the bitcoin blockchain. In: IEEE International Conference on Data Mining Workshops, pp. 55–62. IEEE (2018)
16.
Kalodner, H., Möser, M., Lee, K., Goldfeder, S., Plattner, M., Chator, A., Narayanan, A.: BlockSci: design and applications of a blockchain analysis platform. In: 29th USENIX Security Symposium, pp. 2721–2738. USENIX Association (2020)
17.
Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine Learning, pp. 1188–1196. JMLR (2014)
18.
Lin, Y.J., Wu, P.W., Hsu, C.H., Tu, I.P., Liao, S.W.: An evaluation of bitcoin address classification based on transaction history summarization. In: IEEE International Conference on Blockchain and Cryptocurrency, pp. 302–310. IEEE (2019)
19.
Lischke, M., Fabian, B.: Analyzing the bitcoin network: the first four years. Future Internet 8(1), 7 (2016)CrossRef
20.
Liu, X.F., Ren, H.H., Liu, S.H., Jiang, X.J.: Characterizing key agents in the cryptocurrency economy through blockchain transaction analysis. EPJ Data Sci. 10(1), 21 (2021)CrossRef
21.
Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11) (2008)
22.
Meiklejohn, S., Pomarole, M., Jordan, G., Levchenko, K., McCoy, D., Voelker, G.M., Savage, S.: A fistful of bitcoins: characterizing payments among men with no names. Commun. ACM 59(4), 86–93 (2016)CrossRef
23.
Michalski, R., Dziubałtowska, D., Macek, P.: Revealing the character of nodes in a blockchain with supervised learning. IEEE Access 8, 109639–109647 (2020)CrossRef
24.
Möser, M., Böhme, R., Breuker, D.: An inquiry into money laundering tools in the bitcoin ecosystem. In: APWG eCrime Researchers Summit, pp. 1–14. IEEE (2013)
25.
26.
Narayanan, A., Chandramohan, M., Venkatesan, R., Chen, L., Liu, Y., Jaiswal, S.: graph2vec: learning distributed representations of graphs (2017). arXiv:​1707.​05005
27.
Nerurkar, P., Patel, D., Busnel, Y., Ludinard, R., Kumari, S., Khan, M.K.: Dissecting bitcoin blockchain: empirical analysis of bitcoin network (2009–2020). J. Netw. Comput. Appl. 177, 102940 (2021)CrossRef
28.
Ranshous, S., Joslyn, C.A., Kreyling, S., Nowak, K., Samatova, N.F., West, C.L., Winters, S.: Exchange pattern mining in the bitcoin transaction directed hypergraph. In: International Conference on Financial Cryptography and Data Security, pp. 248–263. Springer (2017)
29.
Reid, F., Harrigan, M.: An analysis of anonymity in the bitcoin system. In: Security and Privacy in Social Networks, pp. 197–223. Springer (2013)
30.
Romiti, M., Judmayer, A., Zamyatin, A., Haslhofer, B.: A deep dive into bitcoin mining pools: an empirical analysis of mining shares. In: Workshop on the Economics of Information Security (2019)
31.
Scherer, P., Lio, P.: Learning distributed representations of graphs with Geo2DR. In: ICML Workshop on Graph Representation Learning and Beyond (2020)
32.
Tironsakkul, T., Maarek, M., Eross, A., Just, M.: Probing the mystery of cryptocurrency theft: an investigation into methods for cryptocurrency tainting analysis. In: Cryptocurrency Research Conference (2019)
33.
Tovanich, N., Soulié, N., Heulot, N., Isenberg, P.: The evolution of mining pools and miners’ behaviors in the bitcoin blockchain. IEEE Trans. Netw. Serv. Manage. (2022)
34.
Vallarano, N., Tessone, C.J., Squartini, T.: Bitcoin transaction networks: an overview of recent results. Front. Phys. 286 (2020)
35.
Weber, M., Domeniconi, G., Chen, J., Weidele, D.K.I., Bellei, C., Robinson, T., Leiserson, C.E.: Anti-money laundering in bitcoin: experimenting with graph convolutional networks for financial forensics. In: KDD Workshop on Anomaly Detection in Finance (2019)
36.
Wu, J., Liu, J., Chen, W., Huang, H., Zheng, Z., Zhang, Y.: Detecting mixing services via mining bitcoin transaction network with hybrid motifs. IEEE Trans. Syst. Man Cybern. Syst. (2021)
37.
Zhang, Y., Wang, J., Luo, J.: Heuristic-based address clustering in bitcoin. IEEE Access 8, 210582–210591 (2020)CrossRef
38.
Zola, F., Eguimendia, M., Bruse, J.L., Urrutia, R.O.: Cascading machine learning to attack bitcoin anonymity. In: IEEE International Conference on Blockchain, pp. 10–17. IEEE (2019)
Metadaten
Titel
Pattern Analysis of Money Flows in the Bitcoin Blockchain
verfasst von
Natkamon Tovanich
Rémy Cazabet
Copyright-Jahr
2023
Buch
Complex Networks and Their Applications XI

Print ISBN: 978-3-031-21126-3

Electronic ISBN: 978-3-031-21127-0

Copyright-Jahr: 2023

DOI
https://doi.org/10.1007/978-3-031-21127-0_36

Premium Partner

    Bildnachweise