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Double-Spending Detection for Fast Bitcoin Payment Based on Artificial Immune

  • Zhengjun Liu
  • Hui Zhao
  • Wen Chen
  • Xiaochun Cao
  • Haipeng Peng
  • Jin Yang
  • Tao Yang
  • Ping Lin
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 768)

Abstract

With the rapid development of Bitcoin, it is frequently used in the scene of fast payment. But the strategy which Bitcoin takes to prevent the double-spending attack is waiting for six confirmations (about one hour), this is not suitable for the fast payment scenarios where service time is about tens of seconds. The default strategy in fast payment is that do not offer the service until a payment transaction is added to the wallet of vendor, which is useless for the detection of double-spending attack. In this paper, an immune-based model is proposed to detect the double-spending attack in the fast Bitcoin payment. This model is composed of many immune-based Bitcoin nodes which include a detection modula and a traditional node. Antigen character is first extracted from a transaction by the detection modula, and initial detectors (mature detectors) are generated based on these antigens. Then, memory detectors and mature detectors are used to detect the double-spending attack, and a mature detector which matches an attack will evolve into a memory detector and be delivered to other immune-based nodes in the Bitcoin network, in order to rapidly detect the double-spending attack. Experimental result shows that this model can efficiently detect double-spending attacks in fast Bitcoin payment.

Keywords

Artificial immune Fast payment Bitcoin Double-spending 

Notes

Acknowledgment

This research is supported by National key research and development program of China (Grant No. 2016YFB0800604 and Grant No. 2016YFB0800605) and Natural Science Foundation of China (Grant No. 61402308 and No. 61572334).

References

  1. 1.
    Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system. Consulted (2009)Google Scholar
  2. 2.
    Currency transactions monitoring report of bitcoin, 18 April 2017. http://if.cert.org.cn/jsp/activitiesDetail2.jsp?id=49
  3. 3.
    Tschorsch, F., Scheuermann, B.: Bitcoin and beyond: a technical survey on decentralized digital currencies. IEEE Commun. Surv. Tutorials, 1 (2016)Google Scholar
  4. 4.
    Rosenfeld, M.: Analysis of hashrate-based double spending. Eprint Arxiv (2014)Google Scholar
  5. 5.
    Sompolinsky, Y., Zohar, A.: Secure High-rate transaction processing in bitcoin. In: Böhme, R., Okamoto, T. (eds.) FC 2015. LNCS, vol. 8975, pp. 507–527. Springer, Heidelberg (2015). doi: 10.1007/978-3-662-47854-7_32 CrossRefGoogle Scholar
  6. 6.
    Sompolinsky, Y., Zohar, A.: Bitcoins security model revisited, May 2016. https://arxiv.org/abs/1605.09193
  7. 7.
    Cnn: Bitcoin’s uncertain future as currency, 4 April 2011. http://www.youtube.com/watch?v=75VaRGdzMM0
  8. 8.
    Karame, G.O., Androulaki, E., Capkun, S.: Two bitcoins at the price of one? double-spending attacks on fast payments in bitcoin. In: Conference on Computer & Communication Security (2012)Google Scholar
  9. 9.
    Karame, G.O., Androulaki, E., Roeschlin, M., Gervais, A., Apkun, S.: Misbehavior in bitcoin: a study of double-spending and accountability. ACM Trans. Inf. Syst. Secur. 18(1), 1–32 (2015)CrossRefGoogle Scholar
  10. 10.
    Bamert, T., Decker, C., Elsen, L., Wattenhofer, R.: Have a snack, pay with bitcoins. In: IEEE Thirteenth International Conference on Peer-To-Peer Computing, pp. 1–5 (2013)Google Scholar
  11. 11.
    Forrest, S., Hofmeyr, S.A., Somayaji, A.: Computer immunology. Immunol. Rev. 216(1), 176–197 (2007)CrossRefGoogle Scholar
  12. 12.
    Forrest, S., Perelson, A.S., Allen, L., Cherukuri, R.: Self-nonself discrimination in a computer. In: Proceedings of the 1994 IEEE Computer Society Symposium on Research in Security and Privacy, pp. 202–212 (1994)Google Scholar
  13. 13.
    A portal of bitcoin, luxembourg s.a., April 2017. https://blockchain.info/
  14. 14.
    Glickman, M., Balthrop, J., Forrest, S.: A machine learning evaluation of an artificial immune system. Evol. Comput. 13(2), 179–212 (2005)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Zhengjun Liu
    • 1
  • Hui Zhao
    • 2
  • Wen Chen
    • 2
  • Xiaochun Cao
    • 3
  • Haipeng Peng
    • 4
  • Jin Yang
    • 2
  • Tao Yang
    • 1
  • Ping Lin
    • 1
  1. 1.College of Computer ScienceSichuan UniversityChengduChina
  2. 2.College of CybersecuritySichuan UniversityChengduChina
  3. 3.Institute of Information EngineeringChinese Academy of SciencesBeijingChina
  4. 4.College of CybersecurityBeijing University of Posts and TelecommunicationsBeijingChina

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