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Toward a Machine Learning and Software Defined Network Approaches to Manage Miners’ Reputation in Blockchain

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Abstract

In blockchain, transactions between parties are regrouped into blocks, in order to be added to the blockchain’s distributed ledger. Miners are nodes of the network that generate new blocks according to the consensus protocol. The miner that adds a valid block to the distributed ledger is rewarded. However, to find a valid block, the miner needs to solve a computationally difficult problem, which makes it difficult to a single miner to gain rewards. Therefore, miners join mining pools, where the powers’ of miners are federated to ensure stable revenues. In public blockchains, access to mining pools is not restricted, which makes mining pools vulnerable to considerable threats such as: block withholding (BWH) attacks and distributed denial of service (DDoS) attacks. In the present work, we propose a new reputation based blockchain named PoolCoin based on a distributed trust model for a mining pools. The trust model used by PoolCoin is inspired from the job market signaling model. The proposed PoolChain blockchain allows pool managers the selection of trusted miners in their mining pools, while miners are able to evaluate them. Furthermore, to detect malicious miners that claim bigger computing capacity, we also provided a machine learning module to estimate the real miners’ capacities. The efficiency of the proposed trust model is studied and the obtained simulation results are presented and discussed. Thus, the model parameters’ are optimized in order to detect and exclude misbehaving miners, while honest miners are maintained in the mining pool.

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References

  1. Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system (2008)

  2. Dinh, T.T.A., Liu, R., Zhang, M., Chen, G., Ooi, B.C., Wang, J.: Untangling blockchain: a data processing view of blockchain systems. IEEE Trans. Knowl. Data Eng. 30(7), 1366–1385 (2018)

    Article  Google Scholar 

  3. Wang, X., et al.: Survey on blockchain for Internet of Things. Comput. Commun. 136, 10–29 (2019)

    Article  Google Scholar 

  4. Bag, S., Sakurai, K.: Yet another note on block withholding attack on bitcoin mining pools. In: Information Security, pp. 167–180 (2016)

    Google Scholar 

  5. Werner, S.M., Pritz, P.J., Zamyatin, A., Knottenbelt, W.J.: Uncle traps: harvesting rewards in a queue-based ethereum mining pool, 070 (2019)

  6. Qin, R., Yuan, Y., Wang, F.-Y.: A novel hybrid share reporting strategy for blockchain miners in PPLNS pools. Decis. Support Syst. 118, 91–101 (2019)

    Article  Google Scholar 

  7. Johnson, B., Laszka, A., Grossklags, J., Vasek, M., Moore, T.: Game-theoretic analysis of DDoS attacks against bitcoin mining pools. In: Financial Cryptography and Data Security, pp. 72–86 (2014)

    Google Scholar 

  8. Haddadou, N., Rachedi, A., Ghamri-Doudane, Y.: A job market signaling scheme for incentive and trust management in vehicular ad hoc networks. IEEE Trans. Veh. Technol. 64(8), 3657–3674 (2015)

    Article  Google Scholar 

  9. Tang, C., Wu, L., Wen, G., Zheng, Z.: Incentivizing honest mining in blockchain networks: a reputation approach. IEEE Trans. Circuits Syst. II Express Briefs 67(1), 117–121 (2019)

    Article  Google Scholar 

  10. Sapirshtein, A., Sompolinsky, Y., Zohar, A.: Optimal selfish mining strategies in bitcoin. In: Financial Cryptography and Data Security, pp. 515–532 (2017)

    Chapter  Google Scholar 

  11. Eyal, I., Sirer, E.G.: Majority is not enough: bitcoin mining is vulnerable. Commun. ACM 61(7), 95–102 (2018)

    Article  Google Scholar 

  12. Liu, X., Wang, W., Niyato, D., Zhao, N., Wang, P.: Evolutionary game for mining pool selection in blockchain networks. IEEE Wirel. Commun. Lett. 7(5), 760–763 (2018)

    Article  Google Scholar 

  13. Yahiatene, Y., Rachedi, A.: Towards a blockchain and software-defined vehicular networks approaches to secure vehicular social network. In: IEEE Conference on Standards for Communications and Networking (CSCN) 2018, 1–7 (2018)

  14. Yu, J., Kozhaya, D., Decouchant, J., Esteves-Verissimo, P.: Repucoin: your reputation is your power. IEEE Trans. Comput. 68(8), 1225–1237 (2019)

    Article  MathSciNet  Google Scholar 

  15. Proof of reputation node Archives—GoChain GoChain. https://gochain.io/tag/proof-of-reputation-node/. Accessed 20 Mar 2020.

  16. Huang, C., Wang, Z., Chen, H., Hu, Q., Zhang, Q., Wang, W., Guan, X.: Repchain: a reputation based secure, fast and high incentive blockchain system via sharding. arXiv preprint arXiv:1901.05741 (2019)

  17. Nojoumian, M., Golchubian, A., Njilla, L., et al.: Incentivizing blockchain miners to avoid dishonest mining strategies by a reputation-based paradigm. In: Science and Information Conference. Springer, Cham, pp. 1118–1134 (2018)

    Google Scholar 

  18. Saraswat, S., Agarwal, V., Gupta, H.P., et al.: Challenges and solutions in Software Defined Networking: a survey. J Netw Comput Appl 141, 23–58 (2019)

    Article  Google Scholar 

  19. Foukas, X., Marina, M.K., Kontovasilis, K.: Software defined networking concepts, software defined mobile networks (SDMN): Beyond LTE Network Architecture, p. 21. (2015)

  20. Rawat, Danda B., Reddy, Swetha R.: Software defined networking architecture, security and energy efficiency: a survey. IEEE Commun. Surveys Tutor. 19(1), 325–346 (2016)

    Article  Google Scholar 

  21. Oktian, Yustus Eko: Distributed SDN controller system: a survey on design choice. Comput. Netw. 111, 100–111 (2017)

    Article  Google Scholar 

  22. Levy, M.: 15-Machine learning at the edge. In: Oshana, R., Kraeling, M. (eds.) Software Engineering for Embedded Systems, 2nd edn, pp. 549–601. Newnes, London (2019)

    Chapter  Google Scholar 

  23. Krestinskaya, O., James, A.P.: Learning algorithms and implementation. In: James, A.P. (ed.) Deep Learning Classifiers with Memristive Networks: Theory and Applications, pp. 91–102. Springer International Publishing, Cham (2020)

    Chapter  Google Scholar 

  24. Shobha, G., Rangaswamy, S.: Chapter 8-Machine learning. In: Gudivada, V.N., Rao, C.R. (eds.) Handbook of Statistics, vol. 38, pp. 197–228. Elsevier, Amsterdam (2018)

    Google Scholar 

  25. Toğaçar, M., Ergen, B., Cömert, Z.: A deep feature learning model for pneumonia detection applying a combination of mRMR feature selection and machine learning models. IRBM (2019)

  26. Hope, T.M.H.: Linear regression. In: Machine Learning. Academic Press, pp. 67–81 (2020)

  27. Zhang, F., O’Donnell, L. J.: Support vector regression. In: Machine Learning, pp. 123–140. Academic Press (2020)

  28. Yang, X.-S.: 8—Neural networks and deep learning. In: Yang, X.-S. (ed.) Introduction to Algorithms for Data Mining and Machine Learning, pp. 139–161. Academic Press, London (2019)

    Chapter  Google Scholar 

  29. Ferrag, M.A., Maglaras, L., Moschoyiannis, S., Janicke, H.: Deep learning for cyber security intrusion detection: approaches, datasets, and comparative study. J Inf Secur Appl 50, 102419 (2020)

    Google Scholar 

  30. Akira, S.: Ethereum—The Next Generation of Cryptocurrency: A Guide to the World of Ethereum. CreateSpace Independent Publishing Platform, Scotts Valley (2018)

    Google Scholar 

  31. Back, A., Corallo, M., Dashjr, L., Friedenbach, M., Maxwell, G., Miller, A., Poelstra, A., Timón, J., Wuille, P.: Enabling blockchain innovations with pegged sidechains, 72. http://www.opensciencereview.com/papers/123/enablingblockchain-innovations-with-pegged-sidechains (2014)

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Kaci, A., Rachedi, A. Toward a Machine Learning and Software Defined Network Approaches to Manage Miners’ Reputation in Blockchain. J Netw Syst Manage 28, 478–501 (2020). https://doi.org/10.1007/s10922-020-09532-1

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  • DOI: https://doi.org/10.1007/s10922-020-09532-1

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