Abstract
At present, there are numerous security vulnerabilities in the smart grid, which seriously threaten the usage environment of the smart grid and the privacy of users. This study addresses this problem by building a vulnerability collaborative defense framework that strengthens smart grid’s resilience against vulnerabilities by utilizing the immutability of blockchain technology and the semantic association capabilities of knowledge graphs, and ensure the data security. The model created by combining these two technologies is examined first in the study. According to the findings, the model increased vulnerability description accuracy by 32.1% and decreased data tampering by 11.4%. Analysis of the collaborative defense framework based on this model later showed that it reduced the vulnerability false positive rate to 4.8% while achieving a 95.7% detection rate for covert vulnerabilities. From the above analysis results, it can be observed that the vulnerability collaborative defense framework proposed in the study can improve the accuracy of vulnerability detection and defense capabilities of the smart grid, thereby providing a secure environment for the smart grid, preventing external interference, and ensuring the accuracy of data.
References
Awadallah A, Eledlebi K, Zemerly MJ, Puthal D, Damiani E, Taha K, Kim TY, Yoo PD, Choo KK, Yim MS, Yeun CY. Artificial intelligence-based cybersecurity for the metaverse: Research challenges and opportunities. IEEE Communications Surveys & Tutorials. 2024, 27(2): 1008–1052. DOI: 10.1109/COMST.2024.3442475.
Liu M, Teng F, Zhang Z, Ge P, Sun M, Deng R, Cheng P, Chen J. Enhancing cyber-resiliency of der-based smart grid: A survey. IEEE Transactions on Smart Grid. 2024, 15(5): 4998–5030. DOI: 10.1109/TSG.2024.3373008.
Almasabi S, Shaf A, Ali T, Zafar M, Irfan M, Alsuwian T. Securing smart grid data with blockchain and wireless sensor networks: A collaborative approach. IEEE Access. 2024, 12(8): 19181–19198. DOI: 10.1109/ACCESS.2024.3361752.
Tomar A, Tripathi S, Arivarasan K. A blockchain-based certificateless aggregate signature scheme for fog-enabled smart grid environment. IEEE Transactions on Green Communications and Networking. 2023, 7(4): 1892–1905. DOI: 10.1109/TGCN.2023.3265608.
Mishchenko D, Oleinikova I, Erdődi L, Pokhrel BR. Multidomain cyber-physical testbed for power system vulnerability assessment. IEEE Access. 2024, 12(11): 38135–38149. DOI: 10.1109/ACCESS.2024.3375401.
Luo Y, Hu Q, Zou B, Zhang Y, Chen T, Wang Q. External vulnerability assessment of power system under attack based on attack-defense game. IEEE Transactions on Power Systems, 2025, 40(5): 4380–4390. DOI: 10.1109/TPWRS.2025.3547067.
Dutta A, Al-Shaer E, Aghaei E, Duan Q, Yasar H. Security Control Grid for Optimized Cyber Defense Planning. IEEE Transactions on Network and Service Management. 2024, 22(1): 913–929, DOI: 10.1109/TNSM.2024.3488011.
Zhang Z, Liu M, Sun M, Deng R, Cheng P, Niyato D, Chow MY, Chen J. Vulnerability of machine learning approaches applied in iot-based smart grid: A review. IEEE Internet of Things Journal. 2024, 11(11): 18951–18975. DOI: 10.1109/JIOT.2024.3349381.
Banik S, Rogers M, Mahajan SM, Emeghara CM, Banik T, Craven R. Survey on vulnerability testing in the smart grid. IEEE Access. 2024, 12(8): 119146–119173. DOI: 10.1109/ACCESS.2024.3449642.
Qi Y, Gu Z, Li A, Zhang X, Shafiq M, Mei Y, Lin K. Cybersecurity knowledge graph enabled attack chain detection for cyber-physical systems. Computers and Electrical Engineering. 2023, 10(108): 108660–108671. DOI: 10.1016/j.compeleceng.2023.108660.
Zhang Y, Chen J, Cheng Z, Shen X, Qin J, Han Y, Lu Y. Edge propagation for link prediction in requirement-cyber threat intelligence knowledge graph. Information Sciences. 2024, 653(5): 119770–11979. DOI: 10.1016/j.ins.2023.119770.
Ren S, Chen S. Large Language Models for Cybersecurity Intelligence, Threat Hunting, and Decision Support. Computer Life. 2025, 13(3): 39–47. DOI: 10.54097/7ysr5k17.
Bayer M, Kuehn P, Shanehsaz R, Reuter C. Cysecbert: A domain-adapted language model for the cybersecurity domain. ACM Transactions on Privacy and Security. 2024, 27(2): 1–20. DOI: 10.1145/3652594.
Ahmad J, Zia MU, Naqvi IH, Chattha JN, Butt FA, Huang T, Xiang W. Machine learning and blockchain technologies for cybersecurity in connected vehicles. Wiley interdisciplinary reviews: data mining and knowledge discovery. 2024, 14(1): 1515–1519. DOI: 10.1002/widm.1515.
Ray RK, Chowdhury FR, Hasan MR. Blockchain applications in retail cybersecurity: Enhancing supply chain integrity, secure transactions, and data protection. Journal of Business and Management Studies. 2024, 6(1): 206–214. DOI: 10.32996/jbms.2024.6.1.13.
Muthulakshmi S, Chitra R. Interplanetary file system and blockchain for secured smart grid networks. Journal of supercomputing, 2024, 80(5): 5900–5922. DOI:10.1007/s11227-023-05680-8.
Jouyban M, Hosseini S. Analytics and measuring the vulnerability of communities for complex network security. International Journal of Data Science and Analytics, 2025, 20(4): 3475–3494. DOI: 10.1007/s41060-024-00673-z.
Luo F, Wang S, Lv Y, Mu R., Fo J, Zhang T. Domain knowledge-enhanced graph reinforcement learning method for Volt/Var control in distribution networks. Applied Energy, 2025, 398(1): 126409–126412. DOI: 10.1016/j.apenergy.2025.126409.
Ren H, Jiang P, Li Q. Machine as a smart service: a hybrid knowledge graph approach. Flexible Services and Manufacturing Journal, 2025, 37(3):750–775. DOI: 10.1007/s10696-024-09558-6.
Liu Q, Jin Y Y, Cao X, Liu X, Zhou X, Zhang Y, Xu X, Qi L. An entity ontology-based knowledge graph embedding approach to news credibility assessment. IEEE Transactions on Computational Social Systems. 2024, 11(4):5308–5318. DOI: 10.1109/TCSS.2023.3342873.
Mohammadi F, Saif M. Blockchain technology in modern power systems: a systematic review. IEEE Systems, Man, and Cybernetics Magazine. 2023, 9(1): 37-47. DOI: 10.1109/MSMC.2022.3201365.
Rajeyyagari S, Saravanan M, Pandey PS, Devi A, Shankar SS. Convolutional Neural network-based African vulture optimization algorithm for the enhancement of cybersecurity in the blockchain-based Smart grid. Multimedia Tools and Applications. 2024, 83(20): 58527–58553. DOI: 10.1007/s11042-023-17805-5.
Li P, Ye D. Vulnerability analysis of distributed state estimator under false data injection attacks. IEEE Transactions on Information Forensics and Security. 2024, 19(5): 5235–5244. DOI: 10.1109/TIFS.2024.3396634.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright (c) 2026 Journal of Cyber Security and Mobility