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IJMLC 2020 Vol.10(2): 352-357 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.2.942

Blockchain Attacks, Analysis and a Model to Solve Double Spending Attack

A. Begum, A. H. Tareq, M. Sultana, M. K. Sohel, T. Rahman, and A. H. Sarwar

Abstract—Blockcahin is such a technology that helps us to use a shared ledger. Although the ledger is in shared manner, the total system is quiet secure. Bitcoin is a crypto currency which uses blockchain technology. Value of blockchain is very high than dollar or some other expensive currency. This is one of the reasons of encouraging theft attack on the blockchain technology. In this paper, we want to show the attacks on blockchain, their targeted area, reason and their possible proposed solution as review. Besides this, Double spending attack is a major attack on blockchain which is occurred twice till now and caused a huge loss of crypto currency. In this paper, we also want to represent the reasons of these attacks and propose one solution that can prevent Double Spending Attack. Our findings will provide some future direction for new researchers and also help the crypto business analysts to predict about present security in the aspects of blockchain network.

Index Terms—Blockchain, bitcoin, attacks, double spending attack & solutions.

The authors are with the Daffodil International University, Dhaka, Bangladesh (e-mail: afsana.swe@ diu.edu.bd, tareq677@diu.edu.bd, mahmuda667@diu.edu.bd, khaledsohel@daffodilvarsity.edu.bd, tasnim.swe@diu.edu.bd, afjal.swe@diu.edu.bd).


Cite: A. Begum, A. H. Tareq, M. Sultana, M. K. Sohel, T. Rahman, and A. H. Sarwar, "Blockchain Attacks, Analysis and a Model to Solve Double Spending Attack," International Journal of Machine Learning and Computing vol. 10, no. 2, pp. 352-357, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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