A Resilient micro-payment infrastructure: an approach based on blockchain technology
DOI:
https://doi.org/10.48129/kjs.v49i1.10578Keywords:
Blockchain technology, ecommerce, micropayment, recovery from cyber attacksAbstract
Micro-payment systems are growing at a rapid pace despite their security weaknesses. Resilient micro-payment infrastructure is a critical asset to digital economy as it helps protecting transactions and extends micro shopping. In this paper, we present a micro payment infrastructure based on blockchain technology that is capable of reducing the complexity of transactions’ verification, reducing losses, and recovering from various cyber attacks. This infrastructure is user trust-aware, in the sense that it builds a trust function capable of providing a real time management of the user’s trust levels based on historic activity and then adapt the level of verification and risk of misconduct. Moreover, three different trust models are developed to provide different estimations of the tokens’ block size to be submitted to the blockchain network for verification and control of the user waiting time. The micropayment infrastructure provides different security services such as authentication, double-spending and double-selling prevention, tokens forging prevention, transaction traceability, and recovery from cyber attack. In addition, its efficiency is improved through the reduction of the verification delay and user waiting time. Finally, a numerical simulation is conducted to assess the performance of the infrastructure.
References
S. T. Ali, D. Clarke, and P. McCorry, “The nuts and bolts
of micropayments: a survey,” CoRR, vol. abs/1710.02964,
, arXiv:1710.02964v1 [cs.CR].
K. Zile and R. Strazdina, “Blockchain use cases and their
feasibility,” Applied Computer Systems, vol. 23, no. 1, pp.
–20, 2018.
H. Dai, H. P. Young, T. J. S. Durant, G. Gong, M. Kang,
H. M. Krumholz, W. L. Schulz, and L. Jiang, “Trialchain:
A blockchain-based platform to validate data
integrity in large, biomedical research studies,” CoRR,
vol. abs/1807.03662, 2018, arXiv:1807.03662v1 [cs.DC].
H.-N. Dai, Z. Zheng, and Y. Zhang, “Blockchain for
internet of things: A survey,” IEEE Internet of Things
J., vol. 6, no. 5, pp. 8076 – 8094, 2019.
A. Ensor, S. Schefer-Wenzl, and I. Miladinovic,
“Blockchains for iot payments: a survey,” in Proc. of
the 2018 IEEE Globecom Workshops (GC Wkshps), Abu
Dhabi, United Arab Emirates, 2018.
S. Makridakis and K. Christodoulou, “Blockchain: Current
challenges and future prospects/applications,” Future
Internet, vol. 11, no. 258, 2019, mDPI.
R. Pass and abhi shelat, “Micropayments for decentralized
currencies,” in CCS ’15 Proc. of the 22nd ACM
SIGSAC Conf. on Computer and Communications Security,
Denver, Colorado, USA, October 2015, pp. 207–218.
F. Rezaeibagha and Y. Mu, “Efficient micropayment
of cryptocurrency from blockchains,” The Computer J.,
vol. 62, no. 4, pp. 507–517, 2018.
Z.-G. Wan, R. H. Deng, D. Lee, and Y. Li, “Microbtc:
Efficient, flexible and fair micropayment for bitcoin using
hash chains,” J. OF COMPUTER SCIENCE AND TECHNOLOGY,
vol. 34, no. 2, pp. 403–415, 2019.
C. Decker and R. Wattenhofer, “A fast and scalable payment
network with bitcoin duplex micropayment channels,”
in Proc. of the Int. Symp. on Stabilization, Safety,
and Security of Distributed Systems (SSS), Edmonton,
Canada, 2015.
J. Poon and T. Dryja, “The bitcoin lightning network:
Scalable off-chain instant payments,” Tech. Rep., 2016,
https://lightning. network/lightning-network-paper. pdf.
D. Zhang, J. Le, N. Mu, and X. Liao, “An anonymous
off-blockchain micropayments scheme for cryptocurrencies
in the real world,” IEEE Transactions on Systems,
Man, and Cybernetics: Systems, pp. 1–11, 2018, dOI:
1109/TSMC.2018.2884289.
E. Heilman, F. Baldimtsi, and S. Goldberg, “Blindly
signed contracts: Anonymous on-blockchain and offblockchain
bitcoin transacations,” in the 20th Int. conf. on
financial cryptography and data security, Proc. of the FC
Int. Workshops, BITCOIN, VOTING, and WAHC,
S. B. Heidelberg, Ed., Christ Church, Barbados, 2016,
pp. 43–60, https://eprint.iacr.org/2016/056.pdf.
A. Xu, M. Li, X. Huang, N. Xue, J. Zhang, and Q. Sheng,
“A blockchain based micro payment system for smart
devices,” Signature, vol. 256, no. 4936, p. 115, 2016.
T. Lundqvist, A. de Blanche, and H. R. H. Andersson,
“Thing-to-thing electricity micro payments using
blockchain technology,” in Proc. of the 2017 Global
Internet of Things Summit (GIoTS), Geneva, Switzerland,
June 2017.
R. Radhakrishnan and B. Krishnamachari, “Streaming
data payment protocol (sdpp) for the internet of things,” in
IEEE Int. Conf. on Internet of Things (iThings) and
IEEE Green Computing and Communications (Green-
Com) and IEEE Cyber, Physical and Social Computing
(CPSCom) and IEEE Smart Data (SmartData), Halifax,
NS, Canada, 2018, pp. 1679–1684.
D. Chen, Z. Zhang, A. Krishnan, and B. Krishnamachari,
“Payflow: Micropayments for bandwidth reservations in
software defined networks,” in IEEE INFOCOM 2019
- IEEE Conf. on Computer Communications Workshops
(INFOCOM WKSHPS), Paris, France, 2019, pp. 26–31.
G. S. Ramachandran, X. Ji, P. Navaney, L. Zheng,
M. Martinez, and B. Krishnamachari, “Motive: micropayments
for trusted vehicular services,” CoRR, vol.
abs/1904.01630, 2019, arXiv:1904.01630v1 [cs.DC].
D. Strugar, R. Hussain, M. Mazzara, V. Rivera, J. Lee,
and R. Mustafin, “On m2m micropayments : A case study
of electric autonomous vehicles,” in Proc. of the 2018
IEEE International Conference on Internet of Things
(iThings) and IEEE Green Computing and Communications
(GreenCom) and IEEE Cyber, Physical and Social
Computing (CPSCom) and IEEE Smart Data (Smart-
Data), Halifax, NS, Canada, Canada, 2018, pp. 1697–
Z. Ye, T. Wen, Z. Liu, X. Song, and C. Fu, “An efficient
dynamic trust evaluation model for wireless sensor
networks,” sensors, vol. 2017, 2017.
Z. Chen, L. Tian, and C. Lin, “Trust model of wireless
sensor networks and its application in data fusion,” sensors,
vol. 17, no. 4, 2017.
J. Duan, D. Gao, C. H. Foh, and V. C. M. Leung,
“Trust and risk assessment approach for access control
in wireless sensor networks,” in Proc. of the 2013 IEEE
th Vehicular Technology Conference (VTC Fall), 2013.
J. ZHAO, J. HUANG, and N. XIONG, “An effective
exponential-based trust and reputation evaluation system
in wireless sensor networks,” Special Section on Artificial
Intelligence and Cognitive Computing for Communication
and sensors, vol. 7, pp. 33 859 – 33 869, 2019.
R. Feng, X. Han, Q. Liu, and N. Yu, “A credible bayesianbased
trust management scheme for wireless sensor networks,”
Int. J. of Distributed Sensor Networks, vol. 2015,
S. Che, R. Feng, X. Liang, and X. Wang, “A lightweight
trust management based on bayesian and entropy for
wireless sensor networks,” Security and communication
networks, vol. 8, no. 2, pp. 168–175, 2015.
Y. Yu, K. Li, W. Zhou, and P. Li, “Trust mechanisms
in wireless sensor networks: Attack analysis and countermeasures,” J. of Network and Computer Applications,
vol. 35, no. 3, pp. 867–880, 2012.
G. Sun, Z. Zhang, B. Zheng, and Y. Li, “Multi-sensor
data fusion algorithm based on trust degree and improved
genetics,” sensors, vol. 19, no. 9, 2019.
W. Alnumay, U. Ghosh, and P. Chatterjee, “A trustbased
predictive model for mobile ad hoc network
in internet of things,” sensors, vol. 19, no. 6, 2019,
doi:10.3390/s19061467.
J. Zhang, Q. Sun, A. Zhou, and J. Li, “A novel trust
update mechanism based on sliding window for trust
management system,” in Proc. of Int. Conf. on Computational
Science and Its Applications (ICCSA 2016),
Beijing, China, 2016, pp. 521–528.
E. Zupancic and B. Zalik, “Data trustworthiness evaluation
in mobile crowdsensing systems with users’ trust
dispositions’ consideration,” sensors, vol. 19, no. 6, 2019.
X. J. Yang, V. V. Unhelkar, K. Li, and J. A. Shah,
“Evaluating effects of user experience and system transtransparency on trust in automation,” in Proc. of the 2017
ACM/IEEE Int. Conf. on Human-Robot Interaction (HRI
’17), Vienna, Austria, 2017, pp. 408–416.
C. Wang, C. Zhang, and X. J. Yang, “Automation reliability
and trust: A bayesian inference approach,” Proc.
of the Human Factors and Ergonomics Society Annual
Meeting, vol. 62, no. 1, pp. 202–206, 2018.