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

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

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)

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

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

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

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)

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)

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)

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)

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

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

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)

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)

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

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

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)

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)

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)

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

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)

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

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)

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)

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)

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)

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

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

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)

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)

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

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)