Parasite Chain Detection in the IOTA Protocol

Authors Andreas Penzkofer, Bartosz Kusmierz, Angelo Capossele, William Sanders, Olivia Saa



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Author Details

Andreas Penzkofer
  • IOTA Foundation, Berlin, Germany
Bartosz Kusmierz
  • Department of Theoretical Physics, Wroclaw University of Science and Technology, Poland
Angelo Capossele
  • IOTA Foundation, Berlin, Germany
William Sanders
  • IOTA Foundation, Berlin, Germany
Olivia Saa
  • Department of Applied Mathematics, Institute of Mathematics and Statistics, University of São Paulo, Brazil

Cite AsGet BibTex

Andreas Penzkofer, Bartosz Kusmierz, Angelo Capossele, William Sanders, and Olivia Saa. Parasite Chain Detection in the IOTA Protocol. In 2nd International Conference on Blockchain Economics, Security and Protocols (Tokenomics 2020). Open Access Series in Informatics (OASIcs), Volume 82, pp. 8:1-8:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/OASIcs.Tokenomics.2020.8

Abstract

In recent years several distributed ledger technologies based on directed acyclic graphs (DAGs) have appeared on the market. Similar to blockchain technologies, DAG-based systems aim to build an immutable ledger and are faced with security concerns regarding the irreversibility of the ledger state. However, due to their more complex nature and recent popularity, the study of adversarial actions has received little attention so far. In this paper we are concerned with a particular type of attack on the IOTA cryptocurrency, more specifically a Parasite Chain attack that attempts to revert the history stored in the DAG structure, also called the Tangle. In order to improve the security of the Tangle, we present a detection mechanism for this type of attack. In this mechanism, we embrace the complexity of the DAG structure by sampling certain aspects of it, more particularly the distribution of the number of approvers. We initially describe models that predict the distribution that should be expected for a Tangle without any malicious actors. We then introduce metrics that compare this reference distribution with the measured distribution. Upon detection, measures can then be taken to render the attack unsuccessful. We show that due to a form of the Parasite Chain that is different from the main Tangle it is possible to detect certain types of malicious chains. We also show that although the attacker may change the structure of the Parasite Chain to avoid detection, this is done so at a significant cost since the attack is rendered less efficient.

Subject Classification

ACM Subject Classification
  • Networks → Security protocols
Keywords
  • Distributed ledger technology
  • cryptocurrency
  • directed acyclic graph
  • security
  • attack detection algorithm

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References

  1. https://github.com/iotaledger/iota.go. Google Scholar
  2. Elsts A., Mitskas E., and Oikonomou G. Distributed Ledger Technology and the Internet of Things: A Feasibility Study, 2019. Google Scholar
  3. Kuśmierz B., Staupe P., and Gal A. Extracting Tangle Properties in Continuous Time via Large-Scale Simulations, 2017. Google Scholar
  4. Iddo Bentov, Pavel Hubáček, Tal Moran, and Asaf Nadler. Tortoise and hares consensus: the meshcash framework for incentive-compatible, scalable cryptocurrencies. Cryptology ePrint Archive, Report 2017/300, 2017. URL: https://eprint.iacr.org/2017/300.
  5. Xavier Boyen, Christopher Carr, and Thomas Haines. Blockchain-Free Cryptocurrencies: A Framework for Truly Decentralised Fast Transactions. Cryptology ePrint Archive, Report 2016/871, 2016. URL: https://eprint.iacr.org/2016/871.
  6. Vitalik Buterin et al. A next-generation smart contract and decentralized application platform, 2014. URL: https://github.com/ethereum/wiki/wiki/White-Paper.
  7. Vitalik Buterin and Virgil Griffith. Casper the Friendly Finality Gadget. ArXiv e-prints, page arXiv:1710.09437, October 2017. URL: http://arxiv.org/abs/1710.09437.
  8. Tai-Yuan Chen, Wei-Ning Huang, Po-Chun Kuo, Hao Chung, and Tzu-Wei Chao. DEXON: A Highly Scalable, Decentralized DAG-Based Consensus Algorithm. ArXiv e-prints, page arXiv:1811.07525, November 2018. URL: http://arxiv.org/abs/1811.07525.
  9. Anton Churyumov. Byteball: A decentralized system for storage and transfer of value, 2016. URL: https://byteball.org/Byteball.pdf.
  10. Kyle Croman, Christian Decker, Ittay Eyal, Adem Efe Gencer, Ari Juels, Ahmed Kosba, Andrew Miller, Prateek Saxena, Elaine Shi, Emin Gün Sirer, et al. On scaling decentralized blockchains. In International Conference on Financial Cryptography and Data Security, pages 106-125. Springer, 2016. Google Scholar
  11. Andrew Cullen, Pietro Ferraro, Christopher King, and Robert Shorten. Distributed ledger technology for iot: Parasite chain attacks. CoRR, 2019. Google Scholar
  12. Ali Dorri, Salil S Kanhere, and Raja Jurdak. Towards an optimized blockchain for iot. In Proceedings of the Second International Conference on Internet-of-Things Design and Implementation, pages 173-178. ACM, 2017. Google Scholar
  13. Tom Everitt and Marcus Hutter. A Topological Approach to Meta-heuristics : Analytical Results on the BFS vs . DFS Algorithm Selection Problem . CoRR, pages 1-58, 2018. URL: http://arxiv.org/abs/arXiv:1509.02709v2.
  14. Yossi Gilad, Rotem Hemo, Silvio Micali, Georgios Vlachos, and Nickolai Zeldovich. Algorand: Scaling byzantine agreements for cryptocurrencies. In Proceedings of the 26th Symposium on Operating Systems Principles, pages 51-68. ACM, 2017. Google Scholar
  15. Huberman Gur, Leshno Jacob D., and Moallemi Ciamac C. Monopoly without a Monopolist: An Economic Analysis of the Bitcoin Payment System, 2017. Google Scholar
  16. K. Karlsson, W. Jiang, S. Wicker, D. Adams, E. Ma, R. van Renesse, and H. Weatherspoon. Vegvisir: A Partition-Tolerant Blockchain for the Internet-of-Things. In Proceedings of the IEEE 38th International Conference on Distributed Computing Systems (ICDCS), pages 1150-1158, July 2018. URL: https://doi.org/10.1109/ICDCS.2018.00114.
  17. Aggelos Kiayias, Alexander Russell, Bernardo David, and Roman Oliynykov. Ouroboros: A provably secure proof-of-stake blockchain protocol. In Annual International Cryptology Conference, pages 357-388. Springer, 2017. Google Scholar
  18. Bartosz Kuśmierz and Alon Gal. Probability of being left behind and probability of becoming permanent tip in the Tangle, 2016. Google Scholar
  19. Yoad Lewenberg, Yonatan Sompolinsky, and Aviv Zohar. Inclusive block chain protocols. In Proceedings of the 19th International Conference on Financial Cryptography and Data Security, pages 528-547. Springer, 2015. Google Scholar
  20. Chenxing Li, Peilun Li, Dong Zhou, Wei Xu, Fan Long, and Andrew Yao. Scaling Nakamoto Consensus to Thousands of Transactions per Second. ArXiv e-prints, page arXiv:1805.03870, May 2018. URL: http://arxiv.org/abs/1805.03870.
  21. Xiaoqi Li, Peng Jiang, Ting Chen, Xiapu Luo, and Qiaoyan Wen. A survey on the security of blockchain systems. Future Generation Computer Systems, August 2017. URL: https://doi.org/10.1016/j.future.2017.08.020.
  22. Loi Luu, Viswesh Narayanan, Chaodong Zheng, Kunal Baweja, Seth Gilbert, and Prateek Saxena. A secure sharding protocol for open blockchains. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pages 17-30. ACM, 2016. Google Scholar
  23. Ujan Mukhopadhyay, Anthony Skjellum, Oluwakemi Hambolu, Jon Oakley, Lu Yu, and Richard Brooks. A brief survey of cryptocurrency systems. In Proceedings of the 14th Annual Conference on Privacy, Security and Trust (PST), pages 745-752. IEEE, 2016. Google Scholar
  24. Satoshi Nakamoto. Bitcoin: A peer-to-peer electronic cash system, 2008. URL: https://bitcoin.org/bitcoin.pdf.
  25. Ferraro P., King C., and Shorten R. Iota-based directed acyclic graphs without orphans, 2019. Google Scholar
  26. Joseph Poon and Thaddeus Dryja. The bitcoin lightning network: Scalable off-chain instant payments, 2016. URL: https://lightning.network/lightning-network-paper.pdf.
  27. Serguei Popov. The tangle, 2015. URL: https://iota.org/IOTA_Whitepaper.pdf.
  28. Meni Rosenfeld. Analysis of hashrate-based double-spending. CoRR, pages 1-13, 2014. URL: http://arxiv.org/abs/arXiv:1402.2009v1.
  29. Yonatan Sompolinsky, Yoad Lewenberg, and Aviv Zohar. Spectre: A fast and scalable cryptocurrency protocol. Cryptology ePrint Archive, Report 2016/1159, 2016. URL: https://eprint.iacr.org/2016/1159.
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