Skip to main content
Log in

Simulation study on the security of consensus algorithms in DAG-based distributed ledger

  • Research Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

Abstract

Due to the advantages of high volume of transactions and low resource consumption, Directed Acyclic Graph (DAG)-based Distributed Ledger Technology (DLT) has been considered a possible next-generation alternative to block-chain. However, the security of the DAG-based system has yet to be comprehensively understood. Aiming at verifying and evaluating the security of DAG-based DLT, we develop a Multi-Agent based IOTA Simulation platform called MAIOTASim. In MAIOTASim, we model honest and malicious nodes and simulate the configurable network environment, including network topology and delay. The double-spending attack is a particular security issue related to DLT. We perform the security verification of the consensus algorithms under multiple double-spending attack strategies. Our simulations show that the consensus algorithms can resist the parasite chain attack and partially resist the splitting attack, but they are ineffective under the large weight attack. We take the cumulative weight difference of transactions as the evaluation criterion and analyze the effect of different consensus algorithms with parameters under each attack strategy. Besides, MAIOTASim enables users to perform large-scale simulations with multiple nodes and tens of thousands of transactions more efficiently than state-of-the-art ones.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Fan C, Ghaemi S, Khazaei H, Chen Y, Musilek P. Performance analysis of the IOTA DAG-based distributed ledger. ACM Transactions on Modeling and Performance Evaluation of Computing Systems, 2021, 6(3): 10

    Article  Google Scholar 

  2. Wang G. SoK: applying blockchain technology in industrial internet of things. Cryptology ePrint Archive. See eprint.iacr.org/2021/776 website, 2021

  3. Alshaikhli M, Elfouly T, Elharrouss O, Mohamed A, Ottakath N. Evolution of Internet of Things from blockchain to IOTA: a survey. IEEE Access, 2022, 10: 844–866

    Article  Google Scholar 

  4. Rathore H, Mohamed A, Guizani M. Blockchain applications for healthcare. In: Mohamed A, ed. Energy Efficiency of Medical Devices and Healthcare Applications. Amsterdam: Elsevier, 2020, 153–166

    Chapter  Google Scholar 

  5. Conti M, Kumar G, Nerurkar P, Saha R, Vigneri L. A survey on security challenges and solutions in the IOTA. Journal of Network and Computer Applications, 2022, 203: 103383

    Article  Google Scholar 

  6. Albshri A, Alzubaidi A, Awaji B, Solaiman E. Blockchain simulators: a systematic mapping study. In: Proceedings of 2022 IEEE International Conference on Services Computing (SCC). 2022, 284–294

  7. Dinh T T A, Wang J, Chen G, Liu R, Ooi B C, Tan K L. BLOCKBENCH: a framework for analyzing private blockchains. In: Proceedings of 2017 ACM International Conference on Management of Data. 2017, 1085–1100

  8. Stoykov L, Zhang K, Jacobsen H A. VIBES: fast blockchain simulations for large-scale peer-to-peer networks. In: Proceedings of the 18th ACM/IFIP/USENIX Middleware Conference: Posters and Demos. 2017, 19–20

  9. Gouda D K, Jolly S, Kapoor K. Design and validation of BlockEval, a blockchain simulator. In: Proceedings of 2021 International Conference on COMmunication Systems & NETworkS (COMSNETS). 2021, 281–289

  10. Lathif M R A, Nasirifard P, Jacobsen H A. CIDDS: a configurable and distributed DAG-based distributed ledger simulation framework. In: Proceedings of the 19th International Middleware Conference (Posters). 2018, 7–8

  11. Deshpande A, Nasirifard P, Jacobsen H A. eVIBES: configurable and interactive ethereum blockchain simulation framework. In: Proceedings of the 19th International Middleware Conference (Posters). 2018, 11–12

  12. Eyal I, Sirer E G. Majority is not enough: bitcoin mining is vulnerable. In: Proceedings of the 18th International Conference on Financial Cryptography and Data Security. 2014, 436–454

  13. Gervais A, Karame G O, Wüst K, Glykantzis V, Ritzdorf H, Capkun S. On the security and performance of proof of work blockchains. In: Proceedings of 2016 ACM SIGSAC Conference on Computer and Communications Security. 2016, 3–16

  14. Zander M, Waite T, Harz D. DAGsim: Simulation of DAG-Based Distributed Ledger Protocols. ACM SIGMETRICS Performance Evaluation Review, 46(3), 118–121.

  15. Wooldridge M, Jennings N R. Intelligent agents: theory and practice. The Knowledge Engineering Review, 1995, 10(2): 115–152

    Article  Google Scholar 

  16. Bruschi F, Rana V, Gentile L, Sciuto D. Mine with it or Sell it: the superhashing power dilemma. ACM SIGMETRICS Performance Evaluation Review, 2019, 46(3): 127–130

    Article  Google Scholar 

  17. Rosa E, D’Angelo G, Ferretti S. Agent-based simulation of blockchains. In: Proceedings of the 19th Asian Simulation Conference on Methods and Applications for Modeling and Simulation of Complex Systems. 2019, 115–126

  18. Serena L, D’Angelo G, Ferretti S. Security analysis of distributed ledgers and blockchains through agent-based simulation. Simulation Modelling Practice and Theory, 2022, 114: 102413

    Article  Google Scholar 

  19. Paulavicius R, Grigaitis S, Filatovas E. An overview and current status of blockchain simulators. In: Proceedings of 2021 IEEE International Conference on Blockchain and Cryptocurrency (ICBC). 2021, 1–3

  20. Wang Q, Yu J, Chen S, Xiang Y. SoK: DAG-based blockchain systems. ACM Computing Surveys, 2023, 55(12): 261

    Article  Google Scholar 

  21. Lerner S D. DagCoin: a cryptocurrency without blocks. See prismic-io.s3.amazonaws.com/dagcoin/f4e531e1-a5db-43b6-930c-14bf705e65ee_Dagcoin_White_Paper.pdf website, 2015

  22. Popov S. The tangle. See assets.ctfassets.net/r1dr6vzfxhev/2t4uxvsIqk0EUau6g2sw0g/45eae33637ca92f85dd9f4a3a218e1ec/iota1_4_3.pdf website, 2018

  23. Churyumov A. Byteball: a decentralized system for storage and transfer of value. See obyte.org/Byteball.pdf website, 2016

  24. Sompolinsky Y, Zohar A. Secure high-rate transaction processing in bitcoin. In: Proceedings of the 19th International Conference on Financial Cryptography and Data Security. 2015, 507–527

  25. LeMahieu C. Nano: a feeless distributed cryptocurrency network. See nano.org website, 2018

  26. Baird L. The Swirlds Hashgraph consensus algorithm: fair, fast, byzantine fault tolerance. See swirlds.com/downloads/SWIRLDS-TR-2016-01.pdf website, 2016

  27. Chohan U W. The double spending problem and cryptocurrencies. DOI: https://doi.org/10.2139/ssrn.3090174. 2017

  28. Li D, Mei H, Shen Y, Su S, Zhang W, Wang J, Zu M, Chen W. ECharts: a declarative framework for rapid construction of web-based visualization. Visual Informatics, 2018, 2(2): 136–146

    Article  Google Scholar 

  29. Banks J, Carson J S, Nelson B L, Nicol D. Discrete-Event System Simulation. 5th ed. Upper Saddle River: Prentice Hall, 2010

    Google Scholar 

  30. Coordicide Team, IOTA Foundation. The coordicide. See files.iota.org/papers/20200120_Coordicide_WP.pdf website, 2019

  31. Harris C R, Millman K J, Van Der Walt S J, Gommers R, Virtanen P, et al. Array programming with NumPy. Nature, 2020, 585(7825): 357–362

    Article  Google Scholar 

  32. Hagberg A, Swart P, S Chult D. Exploring network structure, dynamics, and function using NetworkX. Los Alamos: Technical Report, Los Alamos National Lab. 2008

    Google Scholar 

  33. Kusmierz B, Sanders W, Penzkofer A, Capossele A, Gal A. Properties of the tangle for uniform random and random walk tip selection. In: Proceedings of 2019 IEEE International Conference on Blockchain (Blockchain). 2019, 228–236

  34. Sutton R S, Barto A G. Reinforcement Learning: An Introduction. 2nd ed. Cambridge: MIT Press, 2018

    MATH  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 62071151). The authors would like to thank the IOTA foundation, and express gratitude to TU Munich Application and Middleware Systems (I13) for implementation of tip selection algorithms used in our platform.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Qiong Li.

Additional information

Shuzhe Li received his ME degree from Harbin Institute of Technology, China in 2022. He is currently a PhD candidate in the Faculty of Computing, Harbin Institute of Technology, China since 2022. His research interests is cyberspace security.

Hongwei Xu is a post doctor in the Faculty of Computing, Harbin Institute of Technology, China. His research interests include cryptography and compressed sensing.

Qiong Li received her PhD degrees from Harbin Institute of Technology, China in 2005. She is now working as a Professor in School of Cyberspace Security, Faculty of Computing at Harbin Institute of Technology, China. Her research interests include theory and application of quantum/classical cryptography, etc.

Qi Han received the BS, MS and PhD degrees from Harbin Institute of Technology University, China in 2002, 2004 and 2009, respectively. Currently, he is a professor of Faculty of Computing, Harbin Institute of Technology, China. His research interests include information hiding and forensics, weak signal detection.

Electronic Supplementary Material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, S., Xu, H., Li, Q. et al. Simulation study on the security of consensus algorithms in DAG-based distributed ledger. Front. Comput. Sci. 18, 183704 (2024). https://doi.org/10.1007/s11704-023-2497-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11704-023-2497-y

Keywords

Navigation