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Security risk and response analysis of typical application architecture of information and communication blockchain

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Abstract

As the role of blockchain technology in various industries becomes more and more important, the technical limitations of its consensus mechanism, private key management and smart contracts are gradually emerging. However, security events such as blockchain platform applications are emerging one after another. As a new round of transformative power in the network era, it combines the blockchain with existing technologies to generate new formats and new models. Technology applications and existing cybersecurity regulatory policies have brought new challenges. This paper analyzes the typical application architecture of blockchain technology, and analyzes the security risks of blockchain application architecture from key layers such as storage layer, protocol layer, extension layer, and application layer. This article combines the DOC mechanism with IOT application scenarios to propose a blockchain-based confidential IOT service model (Beekeeper 1.0) that supports first-order homomorphic multiplication, and a blockchain-based confidential IOT service model that supports high-order homomorphic multiplication (Beekeeper 2.0), Beekeeper 2.0 significantly improves Beekeeper 1.0 in server capabilities, verification efficiency, verification key length, and device work diversity. This article finds that the chain code call delay basically increases with the increase in the transaction sending rate. When the transaction sending rate reaches 300TPS, the chain code calling delay only increases a little, and when the transaction sending rate reaches 300TPS, the success rate of the transaction on the chain increases. Finally, the average blockchain access delay increases with the increase in the transaction sending rate. The blockchain access delay is affected by less throughput, and the transaction success rate is stable at 98%.

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References

  1. Li J, Liang G, Liu T (2017) A novel multi-link integrated factor algorithm considering node trust degree for blockchain-based communication[J]. KSII Trans Internet Inf Sys 11(8):3766–3788

    Google Scholar 

  2. Cha SC, Hung SC, Chen JF et al (2017) On the design of a blockchain-based reputation service for android applications[J]. Adv Sci Lett 23(3):2179–2184

    Article  Google Scholar 

  3. Mengelkamp E, Notheisen B, Beer C et al (2018) A blockchain-based smart grid: towards sustainable local energy markets[J]. Computer Sci Res & Develop 33(1–2):207–214

    Article  Google Scholar 

  4. Geng W U, Bo Z, Ran LI et al (2017) Research on the Application of Blockchain in the Integrated Demand Response Resource Transaction [J]. proceedings of the Csee, 37(13): 3717–3728

  5. Yang X, Zhang Y, Lu J et al (2017) Blockchain-based automated demand response method for energy storage system in an energy local network[J]. Zhongguo Dianji Gongcheng Xuebao/proc Chinese Soc Electr Eng 37(13):3703–3716

    Google Scholar 

  6. Schneider FB (2018) Viewpoint Impediments with Policy Interventions to Foster Cybersecurity[J]. communications of the acm, 61(3):36–38

  7. Spindler G (2019) Digitalization and corporate law–a view from Germany[J]. European Co Financial Law 16(1–2):106–148

    Article  Google Scholar 

  8. Zhang J (2016) Walks trajectory tracking of shared information based on consortium blockchain[J]. Revista de la Facultad de Ingenieria 31(12):8–17

    Google Scholar 

  9. Singh M, Kim S (2017) Intelligent vehicle-trust point: reward based intelligent vehicle communication using blockchain[J]. Opt Eng 33(1):701–709

    Google Scholar 

  10. Yermack D (2017) Corporate Governance and Blockchains[J]. Rev Finance 21(1):7–31

    Google Scholar 

  11. CreditCoin: A Privacy-Preserving Blockchain-Based Incentive Announcement Network for Communications of Smart Vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(7):2204–2220.

  12. Yin H, Guo D, Wang K et al (2018) Hyperconnected network: a decentralized trusted computing and networking Paradigm[J]. IEEE Network 32(1):112–117

    Article  Google Scholar 

  13. Savelyev A . Contract law 2.0: ‘Smart’ contracts as the beginning of the end of classic contract law[J]. Information & Communications Technology Law, 2017, 26(2):1–19.

  14. Konoplev AS, Busygin AG, Zegzhda DP (2018) A Blockchain Decentralized Public Key Infrastructure Model[J]. Automatic Control Co ences 52(8):1017–1021

    Article  Google Scholar 

  15. Cavelty MD (2018) Cybersecurity Research Meets Science and Technology Studies[J]. Politics Governance 6(2):22

    Article  Google Scholar 

  16. Kim TK, Kan JM (2018) Sharing the attribute information based on blockchain[J]. J Eng Appl Sci 13(3):771–775

    Google Scholar 

  17. Adoma F (2018) Big Data, Machine Learning and the BlockChain Technology: An Overview[J]. Int J Co Appl 180(28):1–4

    Google Scholar 

  18. Karamitsos I, Papadaki M, Barghuthi NBA (2018) Design of the blockchain smart contract: a use case for real estate[J]. Journal of Information Security 09(3):177–190

    Article  Google Scholar 

  19. Bahga A, Madisetti VK (2016) Blockchain Platform for Industrial Internet of Things[J]. J Softw Eng Appl 09(10):533–546

    Article  Google Scholar 

  20. Weber RH, Studer E (2016) Cybersecurity in the Internet of Things: Legal aspects[J]. Co Law Secur Report 32(5):715–728

    Article  Google Scholar 

  21. Cruz T, Rosa L, Proena J et al (2016) A cyber security detection framework for supervisory control and data acquisition systems[J]. IEEE Trans Industr Inf 12(6):2236–2246

    Article  Google Scholar 

  22. Conteh NY, Schmick PJ (2016) Cybersecurity:risks, vulnerabilities and countermeasures to prevent social engineering attacks[J]. Int J Adv Co Res 6(23):31–38

    Article  Google Scholar 

  23. Finnemore M, Hollis DB (2016) Constructing norms for global cybersecurity[J]. Am J Int Law 110(3):425–479

    Article  Google Scholar 

  24. Gordon LA, Loeb MP, Zhou L (2016) Investing in cybersecurity: insights from the gordon-loeb model[J]. J Inf Secur 07(2):49–59

    Google Scholar 

  25. Mckenna S, Staheli D, Fulcher C et al (2016) BubbleNet: a cyber security dashboard for visualizing patterns[J]. Co Gr Forum 35(3):281–290

    Article  Google Scholar 

  26. Woo PS, Kim BH (2017) Methodology of cyber security assessment in the smart grid[J]. J Electr Eng Technol 12(2):495–501

    Article  Google Scholar 

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Acknowledgements

This work was supported by Grant NO.2019JZZY011101 from the Major scientific and technological innovation projects in Shandong Province to Dianmin Sun.

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Authors and Affiliations

  1. Shandong Luneng Software Technology Co., Ltd, Jinan, 256001, Shandong, China

    Hongwei Zhao

  2. School of Information Engineering, Henan University of Science and Technology, Luoyang, 471000, Henan, China

    Moli Zhang

  3. Key Laboratory of Intelligent Information Processing, Institute of Computer Technology, Chinese Academy of Sciences, Beijing, China

    Shi Wang

  4. Scientific Research Department, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250117, Shandong, China

    Entang Li

  5. State Key Laboratory of High-End & Storage Technology, Inspur Electronic Information Industry Co., Ltd, Jinan, 250013, Shandong, China

    Zhenhua Guo

  6. Department of Thoracic Surgery, Shandong Cancer Hospital and Institute, Shandong Cancer Hospital Affiliated to Shandong University, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, 250117, Shandong, China

    Dianmin Sun

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  1. Hongwei Zhao
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  2. Moli Zhang
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  3. Shi Wang
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Correspondence to Dianmin Sun.

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Zhao, H., Zhang, M., Wang, S. et al. Security risk and response analysis of typical application architecture of information and communication blockchain. Neural Comput & Applic 33, 7661–7671 (2021). https://doi.org/10.1007/s00521-020-05508-z

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  • DOI: https://doi.org/10.1007/s00521-020-05508-z

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