Osman Biçer, Prashanthi Ramachandran, Nandini Agrawal, Alptekin Küpçü

BlockSim-Net: a network-based blockchain simulator

Since its proposal by Eyal and Sirer (CACM ’13), selfish mining attacks on proof-of-work blockchains have been studied extensively. The main body of this research aims at both studying the extent of its impact and defending against it. Yet, before any practical defense is deployed in a real world blockchain system, it needs to be tested for security and dependability. However, real blockchain systems are too complex to conduct any test on or benchmark the developed protocols. Instead, some simulation environments have been proposed recently, such as BlockSim (Maher et al., SIGMETRICS Perform. Eval. Rev. ’19), which is a modular and easy-to-use blockchain simulator. However, BlockSim’s structure is insufficient to capture the essence of a real blockchain network, as the simulation of an entire network happens over a single CPU. Such a lack of decentralization can cause network issues such as propagation delays being simulated in an unrealistic manner. In this work, we propose BlockSim-Net, a modular, efficient, high performance, distributed, network-based blockchain simulator that is parallelized to better reflect reality in a blockchain simulation environment.

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[1] Etemad M, Küpçü A. Efficient Key Authentication Service for Secure End-to-end Communications. ProvSec 2015. doi: 10.1007/978-3-319-26059-4_10
[2] Allessie D, Sobolewski M, Vaccari L. Blockchain for digital government. Publications Office of the European Union 2019. doi: 10.13140/RG.2.2.34874.85449
[3] Singh P K, Singh R, Nandi S K, Nandi S. Managing Smart Home Appliances with Proof of Authority and Blockchain. Innovations for Community Services 2019; (pp.221-232). doi: 10.1007/978-3-030-22482-0_16
[4] Mashamba-Thompson T P, Crayton E D. Blockchain and Artificial Intelligence Technology for Novel Coronavirus Disease 2019 Self-Testing. Diagnostics 2020; 10 (198). doi: 10.3390/diagnostics10040198
[5] Garay J A, Kiayias A, Leonardos N. The Bitcoin Backbone Protocol: Analysis and Applications. EUROCRYPT 2015. doi: 10.1007/978-3-662-46803-6_10
[6] Eyal I, Sirer E G. Majority is Not Enough: Bitcoin Mining is Vulnerable. Commun. ACM 2013; 61 (7). doi: 10.1145/3212998
[7] Sapirshtein A, Sompolinsky Y, Zohar A. Optimal Selfish Mining Strategies in Bitcoin. Financial Cryptography 2017. doi: 10.1007/978-3-662-54970-4_30
[8] Nayak K, Kumar S, Miller A, Shi E. Stubborn Mining: Generalizing Selfish Mining and Combining with an Eclipse Attack. IEEE EuroS&P 2016. doi: 10.1109/EuroSP.2016.32
[9] Heilman E. One Weird Trick to Stop Selfish Miners: Fresh Bitcoins, A Solution for the Honest Miner. Financial Cryptography 2014 (Poster Abstract). doi: 10.1007/978-3-662-44774-1_12
[10] Zhang R, Preneel B. Publish or Perish: A Backward-Compatible Defense Against Selfish Mining in Bitcoin. CT-RSA 2017. doi: 10.1007/978-3-319-52153-4_16
[11] Stoykov L, Zhang K, Jacobsen H A. VIBES: fast blockchain simulations for large-scale peer-to-peer networks. ACM/IFIP/USENIX Middleware Conference: Posters and Demos 2017. doi: 10.1145/3155016.3155020
[12] Aoki Y, Otsuki K, Kaneko T, Banno R, Shudo, K. SimBlock: A Blockchain Network Simulator. IEEE INFOCOM 2019. doi: 10.1109/INFCOMW.2019.8845253
[13] Alharby M, van Moorsel A. BlockSim: An Extensible Simulation Tool for Blockchain Systems. Frontiers in Blockchain 2020; 3. doi: 10.3389/fbloc.2020.00028
[14] Gervais A, Karame G O, Wüst K, Glykantzis V, Ritzdorf H, Capkun S. On the Security and Performance of Proof of Work Blockchains. ACM Conference on Computer and Communications Security 2016. doi: 10.1145/2976749.2978341
[15] Wang T, Liew S C, Zhang S. When blockchain meets AI: Optimal mining strategy achieved by machine learning. Int. J. Intell. Syst. 2021. doi: 10.1002/int.22375
[16] Bissias G, Levine B. Bobtail: Improved Blockchain Security with Low-Variance Mining. The Network and Distributed System Security Symposium 2020.
[17] Sompolinsky Y, Zohar A. Secure High-Rate Transaction Processing in Bitcoin. Financial Cryptography 2015. doi: 10.1007/978-3-662-47854-7_32