The Science and Technology Innovation Project of National Energy Group “Research and Practice Project on Elastic Security System Based on 5G Kite Private Network”(E621000029)
Bao Haibin,Shi Jianyun,Zhang Jianchang,et al.Elastic security analysis method for multi-source data in 5G kite private network based on integration of communication, sensing and computation[J].Telecommunications Science,2026,42(01):53-64.
Bao Haibin,Shi Jianyun,Zhang Jianchang,et al.Elastic security analysis method for multi-source data in 5G kite private network based on integration of communication, sensing and computation[J].Telecommunications Science,2026,42(01):53-64. DOI: 10.11959/j.issn.1000-0801.2026002.
Elastic security analysis method for multi-source data in 5G kite private network based on integration of communication, sensing and computation
With the advancement of communication technologies toward 6G
the integration of communication
sensing and computation has become a critical characteristic of next-generation power private networks. In scenarios where 5G kite private networks are deployed in power plant production control areas
challenges including difficulties in cross-domain association of multi-source data
poor adaptability in dynamic association rule mining
and insufficient real-time security protection have been identified. To address these issues
an elastic security analysis method for multi-source data oriented to 5G-Advanced dvanced integrated sensing-communication-computation private networks was proposed. This method leveraged the 5G private network as the communication foundation
with perceptual data collected by sensors being integrated to construct a multi-source data fusion processing framework
enabling computational analysis of cross-domain data. By combining Spearman rank correlation analysis with time-series FP-Growth algorithms and incorporating an exponential decay time-weighted model
security risk association rules with time constraints were effectively mined. Simulation verification shows that this method significantly improves rule coverage for existing threats while effectively reducing the false alarm rate. The method well adapts to the dynamic data characteristics and integrated sensing-communication-computation synergy of 5G kite private networks
providing technical support for the secure and continuous operation of core production services in the power industry.
Xiao Y , Fei Z J , Zheng K H , et al . Overview of visualization of power grid operation state [J ] . Journal of Computer-Aided Design & Computer Graphics , 2019 , 31 ( 10 ): 1750 - 1758 .
Lu J C , Fan W G . Application of data mining technology in electric power enterprises in the era of big data [J ] . Guangdong Electric Power , 2014 , 27 ( 9 ): 88 - 94 .
Zou H K , Dong Q J , Wu B T . Research on operation optimization and fault early warning of UHV power grid based on big data analysis [J ] . Computer Programming Skills & Maintenance , 2024 ( 12 ): 111 - 113, 125 .
Pan D S . Application of data mining technology in computer network intrusion detection [J ] . Journal of Hubei University of Science and Technology , 2012 , 32 ( 12 ): 58 - 59 .
Cai R , Yang X , Tian J , et al . Missing data filling method of power grid based on correlation analysis and generating countermeasure network [J ] . Jiangsu Electrical Engineering , 2024 , 43 ( 1 ): 229 - 237 .
Jiang Y W , Li L , Li Z W , et al . Text information mining method of power transformer operation and maintenance based on deep semantic learning [J ] . Proceedings of the CSEE , 2019 , 39 ( 14 ): 4162 - 4171 .
Peng X S , Deng D Y , Cheng S J , et al . Key technologies of power big data for smart grid applications [J ] . Proceedings of the CSEE , 2015 , 35 ( 3 ): 503 - 511 .
Kang S . Research on causality and correlation analysis methods for distribution network big data [D ] . Lanzhou : Northwest Normal University , 2024 .
Huang J X , Lin Z , Luo Z , et al . Mining method of SER event set in converter station based on association rule algorithm [J ] . Science Technology and Engineering , 2022 , 22 ( 8 ): 3152 - 3159 .
Hong J H , Li J B , Qiu X J , et al . Numerical correlation analysis of power grid construction project based on apriori algorithm [C ] // Proceedings of the 2021 6th Asia Conference on Power and Electrical Engineering (ACPEE) . Piscataway : IEEE Press , 2021 : 361 - 364 .
Rao S , Gupta P . Implementing improved algorithm over APRIORI data mining association rule algorithm [J ] . International Journal of Computer Science and Technology , 2012 , 3 ( 1 ): 489 - 493 .
Huang J X , Lin Z , Liu K Z , et al . Mining analysis method of association rules considering massive events of converter station [J ] . Power System Protection and Control , 2022 , 50 ( 12 ): 117 - 125 .
Hipp J , Güntzer U , Nakhaeizadeh G . Algorithms for association rule mining: a general survey and comparison [J ] . ACM SIGKDD Explorations Newsletter , 2000 , 2 ( 1 ): 58 - 64 .
Lu Y F , Feng G P , Lu B B . Analysis of power grid fault risk factors based on association rule mining technology [J ] . Engineering Journal of Wuhan University , 2024 , 57 ( 6 ): 792 - 797 .
Zhang L L , Wang W J , Zhang Y Q . Privacy preserving association rule mining: taxonomy, techniques, and metrics [J ] . IEEE Access , 2019 , 7 : 45032 - 45047 .