机器学习在区块链上的应用——工程计算安全性的新范式

Zhiming Dong; Weisheng Lu

doi:10.1016/j.eng.2024.11.026

PDF(3156 KB)

工程（英文） ›› 2025, Vol. 47 ›› Issue (4) : 250-263. DOI: 10.1016/j.eng.2024.11.026

研究论文

机器学习在区块链上的应用——工程计算安全性的新范式

Zhiming Dong ,
Weisheng Lu ^*

作者信息 +

Machine Learning on Blockchain (MLOB): A New Paradigm for Computational Security in Engineering

Zhiming Dong ,
Weisheng Lu ^*

Author information +

History +

Abstract

Machine learning (ML) has been increasingly adopted to solve engineering problems with performance gauged by accuracy, efficiency, and security. Notably, blockchain technology (BT) has been added to ML when security is a particular concern. Nevertheless, there is a research gap that prevailing solutions focus primarily on data security using blockchain but ignore computational security, making the traditional ML process vulnerable to off-chain risks. Therefore, the research objective is to develop a novel ML on blockchain (MLOB) framework to ensure both the data and computational process security. The central tenet is to place them both on the blockchain, execute them as blockchain smart contracts, and protect the execution records on-chain. The framework is established by developing a prototype and further calibrated using a case study of industrial inspection. It is shown that the MLOB framework, compared with existing ML and BT isolated solutions, is superior in terms of security (successfully defending against corruption on six designed attack scenario), maintaining accuracy (0.01% difference with baseline), albeit with a slightly compromised efficiency (0.231 second latency increased). The key finding is MLOB can significantly enhances the computational security of engineering computing without increasing computing power demands. This finding can alleviate concerns regarding the computational resource requirements of ML–BT integration. With proper adaption, the MLOB framework can inform various novel solutions to achieve computational security in broader engineering challenges.

Keywords

Engineering computing / Machine learning / Blockchain / Blockchain smart contract / Deployable framework

引用本文

EndNote

Ris (Procite)

Bibtex

导出引用

Zhiming Dong, Weisheng Lu. 机器学习在区块链上的应用——工程计算安全性的新范式. Engineering. 2025, 47(4): 250-263 https://doi.org/10.1016/j.eng.2024.11.026

参考文献

原文顺序 | 文献年度倒序 | 文中引用次数倒序

[1]	Buede DM, Miller WD.The engineering design of systems: models and methods.John Wiley & Sons, Hoboken (2016)
[2]	Sinha R, Paredis CJ, Liang VC, Khosla PK.Modeling and simulation methods for design of engineering systems.J Comput Inf Sci Eng 2001; 1(1):84-91.
[3]	Nagrath I, Gopal M.Control systems engineering.New Age International, Hong Kong (2006)
[4]	Sioshansi R, Conejo AJ.Optimization in engineering.Springer International Publishing, Cham (2017)
[5]	Willard J, Jia X, Xu S, Steinbach M, Kumar V.Integrating scientific knowledge with machine learning for engineering and environmental systems.ACM Comput Surv 2023; 55(4):1-37.
[6]	Penumuru DP, Muthuswamy S, Karumbu P.Identification and classification of materials using machine vision and machine learning in the context of Industry 4.0.J Intell Manuf 2020; 31(5):1229-1241.
[7]	Frank M, Drikakis D, Charissis V.Machine-learning methods for computational science and engineering.Computation 2020; 8(1):15.
[8]	Masana M, Liu X, Twardowski B, Menta M, Bagdanov AD, Van J De Weijer.Class-incremental learning: survey and performance evaluation on image classification.IEEE Trans Pattern Anal Mach Intell 2023; 45(5):5513-5533.
[9]	Jeong D.Artificial intelligence security threat, crime, and forensics: taxonomy and open issues.IEEE Access 2020; 8:184560-184574.
[10]	Nadeem A, Vos D, Cao C, Pajola L, Dieck S, Baumgartner R, et al.SoK: explainable machine learning for computer security applications.In: Proceedings of the 2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P); 2023 Jul 3–7; Delft, Netherlands. New York City: IEEE; 2023. p. 221–40.
[11]	Qadir S, Noor B.Applications of machine learning in digital forensics.In: Proceedings of the 2021 International Conference on Digital Futures and Transformative Technologies (ICoD T2); 2021 May 20–21; Islamabad, Pakistan. New York City: IEE E; 2021. p. 1–8.
[12]	Newhart KB, Marks CA, Rauch-Williams T, Cath TY, Hering AS.Hybrid statistical-machine learning ammonia forecasting in continuous activated sludge treatment for improved process control.J Water Process Eng 2020; 37:101389.
[13]	Lu W, Lou J, Wu L.Combining smart construction objects-enabled blockchain oracles and signature techniques to ensure information authentication and integrity in construction.J Comput Civ Eng 2023; 37(6):04023031.
[14]	Venkatesan C, Karthigaikumar P, Varatharajan R.A novel LMS algorithm for ECG signal preprocessing and KNN classifier based abnormality detection.Multimedia Tools Appl 2018; 77(8):10365-10374.
[15]	Lu W, Wu L, Zhao R, Li X, Xue F.Blockchain technology for governmental supervision of construction work: learning from digital currency electronic payment systems.J Constr Eng Manage 2021; 147(10):04021122.
[16]	Lu W, Wu L, Zhao R.Rebuilding trust in the construction industry: a blockchain-based deployment framework.Int J Constr Manag 2023; 23(8):1405-1416.
[17]	Papernot N, McDaniel P, Sinha A, Wellman MP.SoK: security and privacy in machine learning.In: Proceedings of the 2018 IEEE European Symposium on Security and Privacy (EuroS&P); 2018 Apr 24–26; London, UK. New York City: IEEE; 2018. p. 399–414.
[18]	Currie R.Software engineer accused of stealing $300k from employer was ‘inspired by office space’ [Internet].San Francisco: Situation Publishing; 2023 Jan 13 [cited 2023 Feb 6]. Available from: https://www.theregister.com/2023/01/13/office_space_zulily_theft/?td=rt-3a.
[19]	Fang M, Cao X, Jia J, Gong NZ.Local model poisoning attacks to byzantine-robust federated learning.In: Proceedings of the 29th USENIX Conference on Security Symposium; 2020 Aug 12–14; Berkeley, C A, US A. Berkeley: The USENIX Association; 2020. p. 1623–40.
[20]	Lee JH, Shin J, Realff MJ.Machine learning: overview of the recent progresses and implications for the process systems engineering field.Comput Chem Eng 2018; 114:111-121.
[21]	Guo J, Gao H, Liu Z, Huang F, Zhang J, Li X, et al.ICRA: an intelligent clustering routing approach for UAV ad hoc networks.IEEE Trans Intell Transp Syst 2023; 24(2):2447-2460.
[22]	Yuan L, Lu W, Xue F, Li M.Building feature‐based machine learning regression to quantify urban material stocks: a Hong Kong study.J Ind Ecol 2023; 27(1):336-349.
[23]	Yuan L, Lu W, Wu Y.Characterizing the spatiotemporal evolution of building material stock in China’s greater bay area: a statistical regression method.J Ind Ecol 2023; 27(6):1553-1566.
[24]	Guo J, Li X, Liu Z, Ma J, Yang C, Zhang J, et al.Trove: a context-awareness trust model for vanets using reinforcement learning.IEEE Internet Things J 2020; 7(7):6647-6662.
[25]	Dong Z, Chen J, Lu W.Computer vision to recognize construction waste compositions: a novel boundary-aware transformer (BAT) model.J Environ Manage 2022; 305:114405.
[26]	Dong Z, Wang J, Cui B, Wang D, Wang X.Patch-based weakly supervised semantic segmentation network for crack detection.Constr Build Mater 2020; 258:120291.
[27]	Parashar A, Parashar A, Shabaz M, Gupta D, Sahu AK, Khan MA.Advancements in artificial intelligence for biometrics: a deep dive into model-based gait recognition techniques.Eng Appl Artif Intell 2024; 130:107712.
[28]	Asmitha P, Rupa C, Nikitha S, Hemalatha J, Sahu AK.Improved multiview biometric object detection for anti spoofing frauds.Multimedia Tools Appl 2024; 83(33):80161.
[29]	Teoh YK, Gill SS, Parlikad AK.Iot and fog computing based predictive maintenance model for effective asset management in Industry 4.0 using machine learning.IEEE Internet Things J 2021; 10(3):2087-2094.
[30]	L Găzăroiu, Andronie M, Iatagan M, Geam Mănu, Dijm Iărescu.Deep learning-assisted smart process planning, robotic wireless sensor networks, and geospatial big data management algorithms in the internet of manufacturing things.ISPRS Int J Geoinf 2022; 11(5):277.
[31]	Li M, Xue F, Wu Y, Yeh AG.A room with a view: automatic assessment of window views for high-rise high-density areas using city information models and deep transfer learning.Landsc Urban Plan 2022; 226:104505.
[32]	Chan IY, Liu AM.Effects of neighborhood building density, height, greenspace, and cleanliness on indoor environment and health of building occupants.Build Environ 2018; 145:213-222.
[33]	Leung M, Wang C, Chan IY.A qualitative and quantitative investigation of effects of indoor built environment for people with dementia in care and attention homes.Build Environ 2019; 157:89-100.
[34]	Lu W, Wu L, Xu J, Lou J.Construction E-inspection 2.0 in the COVID-19 pandemic era: a blockchain-based technical solution.J Manage Eng 2022; 38(4):04022032.
[35]	Solanki S, Solanki AD.Review of deployment of machine learning in blockchain methodology.Int Res J Adv Sci Hub 2020; 2(9):14-20.
[36]	Shinde R, Nilakhe O, Pondkule P, Karche D, Shendage P.Enhanced road construction process with machine learning and blockchain technology.In: Proceedings of the 2020 International Conference on Industry 4.0 Technology (I4Tech); 2020 Feb 13–15, Pune, India. New York City: IEE E; 2020. p. 207–10.
[37]	Wong P, Chia F, Kiu M, Lou E.The potential of integrating blockchain technology into smart sustainable city development.In: Proceedings of the IOP Conference Series: Earth and Environmental Science; 2020 Sep 18–20; Changsha, China. Bristol: IOP Publishing; 2020. p. 012020.
[38]	Xu J, Lu W, Wu L, Lou J, Li X.Balancing privacy and occupational safety and health in construction: a blockchain-enabled P-OSH deployment framework.Saf Sci 2022; 154:105860.
[39]	Rk K, Kallapu B, Dodmane R, Thota S, Sahu AK.Enhancing cloud communication security: a blockchain-powered framework with attribute-aware encryption.Electronics (Basel) 2023; 12(18):3890.
[40]	Podder D, Deb S, Banik D, Kar N, Sahu AK.Robust medical and color image cryptosystem using array index and chaotic S-box.Cluster Comput 2024; 27(4):4321.
[41]	Jovanovic Z, Hou Z, Biswas K, Muthukkumarasamy V.Robust integration of blockchain and explainable federated learning for automated credit scoring.Comput Netw 2024; 243:110303.
[42]	Li X, Zeng J, Chen C, Chi H, Shen GQ.Smart work package learning for decentralized fatigue monitoring through facial images.Comput Aided Civ Infrastruct Eng 2023; 38(6):799-817.
[43]	Kasyap H, Tripathy S.Privacy-preserving and byzantine-robust federated learning framework using permissioned blockchain.Expert Syst Appl 2024; 238:122210.
[44]	Wu L, Lu W, Chen C.Strengths and weaknesses of client-server and peer-to-peer network models in construction projects.Int J Constr Manag 2023; 24(12):1349-1363.
[45]	Peethambaran G, Naikodi C, Suresh L.An ensemble learning approach for privacy–quality–efficiency trade-off in data analytics.In: Proceedings of the 2020 International Conference on Smart Electronics and Communication (ICOSE C); 2020 Sep 10–12; Trichy, India. New York City: IEE E; 2020. p. 228–35.
[46]	Cooper AF, Levy K, De Sa C.Regulating accuracy-efficiency trade-offs in distributed machine learning systems.2020. SSR N 3650497.
[47]	Al-Marridi AZ, Mohamed A, Erbad A.Reinforcement learning approaches for efficient and secure blockchain-powered smart health systems.Comput Netw 2021; 197:108279.
[48]	Jiang R, Li J, Bu W, Shen X.A blockchain-based trustworthy model evaluation framework for deep learning and its application in moving object segmentation.Sensors 2023; 23(14):6492.
[49]	Feng X, Li L, Wang T, Xu W, Zhang J, Wei B, et al.CoBC: a blockchain-based collaborative inference system for the internet of things.IEEE Internet Things J 2023; 10(24):21389-21400.
[50]	Wang SJ, Pei K, Yang J.SmartInv: multimodal learning for smart contract invariant inference.In: Proceedings of the 2024 IEEE Symposium on Security and Privacy (S P); 2024 May 19–23; San Francisco, C A, US A. New York City: IEE E; 2024.
[51]	Adel K, Elhakeem A, Marzouk M.Decentralizing construction ai applications using blockchain technology.Expert Syst Appl 2022; 194:116548.
[52]	Amershi S, Begel A, Bird C, DeLine R, Gall H, Kamar E, et al.Software engineering for machine learning: a case study.In: Proceedings of the 2019 IEE E/AC M 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEI P); 2019 May 25–31; Montreal, Q C, Canada. New York City: IEE E; 2019. p. 291–300.
[53]	Bertolini M, Mezzogori D, Neroni M, Zammori F.Machine learning for industrial applications: a comprehensive literature review.Expert Syst Appl 2021; 175:114820.
[54]	Kirillov A, Mintun E, Ravi N, Mao H, Rolland C, Gustafson L, et al.Segment anything.2023. arXiv: 2304.02643.
[55]	Gerard C.Practical machine learning in JavaScript: TensorFlow.js for web developers.Springer, Berlin 2021; 25-43.
[56]	Wu H, Zhong B, Li H, Guo J, Wang Y.On-site construction quality inspection using blockchain and smart contracts.J Manage Eng 2021; 37(6):04021065.
[57]	Ongaro D, Ousterhout J.In search of an understandable consensus algorithm.In: Proceedings of the 2014 USENIX Annual Technical Conference (USENIX ATC 14); 2014 Jun 19–20; Philadelphia, P A, US A. Berkeley: The USENIX Association; 2014. p. 305–19.
[58]	Chen LC, Papandreou G, Schroff F, Adam H.Rethinking atrous convolution for semantic image segmentation.2017. arXiv: 1706.05587.
[59]	Howard A, Sandler M, Chu G, Chen LC, Chen B, Tan M, et al.Searching for mobilenetv3.In: Proceedings of the IEEE/CVF International Conference on Computer Vision; 2019 Oct 27–Nov 2; Seoul, Republic of Korea. New York City: IEE E; 2019. p. 1314–24.
[60]	Dobbertin H.Cryptanalysis of MD5 compress.In: Proceedings of the EUROCRYPT’ 96 Rump Session; 1996 May 12–16; Zaragoza, Spain. Bellevue: International Association for Cryptologic Research; 1996. p. 9671–82.
[61]	Anderson JC, Lehnardt J, Slater N.CouchDB: the definitive guide: time to relax.editors. Sebastopol: O’Reilly Media; 2010.
[62]	Savsunenko O.How tensorflow’s tf.image. resize stole 60 days of my life [Internet]. Edwards: HackerNoon; 2018 Jan 23 [cited 2023 Dec 14]. Available from: https://hackernoon.com/how-tensorflows-tf-image-resize-stole-60-days-of-my-life-aba5eb093f35.
[63]	Zheng P, Li S, Xia L, Wang L, Nassehi A.A visual reasoning-based approach for mutual-cognitive human-robot collaboration.CIRP Ann 2022; 71(1):377-380.
[64]	Guo J, Liu Z, Tian S, Huang F, Li J, Li X, et al.TFL-DT: a trust evaluation scheme for federated learning in digital twin for mobile networks.IEEE J Sel Areas Comm 2023; 41(11):3548-3560.
[65]	Miao J, Yang Z, Fan L, Yang Y.FedSeg: class-heterogeneous federated learning for semantic segmentation.In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition;2023 Jun 17–24; Vancouver, B C, Canada. New York City: IEE E; 2023. p. 8042–52.
[66]	Hua H, Li Y, Wang T, Dong N, Li W, Cao J.Edge computing with artificial intelligence: a machine learning perspective.ACM Comput Surv 2023; 55(9):1-35.
[67]	Li L, Fan Y, Tse M, Lin KY.A review of applications in federated learning.Comput Ind Eng 2020; 149:106854.
[68]	Kayikci S, Khoshgoftaar TM.Blockchain meets machine learning: a survey.J Big Data 2024; 11(1):9.