Mathematical modelling of abnormal account detection on social media platform based on improved edge weight

被引：0
作者：
Han Y. [1 ]
机构：
[1] Department of Basic Courses, Shaanxi Open University, Xi’an
来源：
International Journal of Web Based Communities | 2023年 / 19卷 / 2-3期
关键词：
abnormal account; anomaly detection; edge weights; node status; social graph;
D O I：
10.1504/ijwbc.2023.131400
中图分类号：
学科分类号：
摘要：
In order to overcome the problems of traditional methods such as large time consumption, high missed detection rate and low recall rate of detection results, a mathematical modelling method of abnormal account detection on social media platform based on improved edge weight was proposed. The social media platform is regarded as a directed social graph, the node states in the social graph are judged, and the edge weight of the social graph is used to calculate the edge potential. First, take the improved edge weight processing results as the operation constraints of the model. Then, build the abnormal account detection mathematical model. Finally, input the user account information into the model, and output the abnormal account detection results. The experimental results show that the maximum detection time consumption of the proposed method is only 0.88 min, the highest missed detection rate is only 2.9%, and the lowest recall rate can reach 98.49%. Copyright © 2023 Inderscience Enterprises Ltd.
引用
收藏
页码：187 / 197
页数：10
相关论文
共 15 条
  • [1] Chen L., Ruan S., Chen X., Wang H., Research on intelligent detection of social media robot accounts, Netinfo Security, 31, 9, pp. 96-100, (2019)
  • [2] Li H., Zhang Y., Lightweight abnormal account detection scheme based on body-shadow relation, Journal of LanZhou University of Arts And Science (Natural Sciences Edition), 33, 3, pp. 30-32, (2019)
  • [3] Mu R., Network domain named user abnormal access behavior information precision identification emulation, Computer Simulation, 35, 7, pp. 339-342, (2018)
  • [4] Qiu G., Yu H., On the legal attribute and protection principle of social network account, Journal of Anhui Agricultural University (Social Sciences), 28, 4, pp. 75-81, (2019)
  • [5] Qu Q., Yu H., Huang R., Research progress of abnormal user detection technology in social network, Chinese Journal of Network and Information Security, 4, 3, pp. 13-23, (2018)
  • [6] Song C., Yu K., Wu X., Fake account detection method in online social network based on improved edge weighted paired Markov random field model, Computer Science, 47, 2, pp. 251-255, (2020)
  • [7] Sun X., Zhang C., Ren F., User emotion modeling and anomaly detection based on social media, Journal of Chinese Information Processing, 32, 4, pp. 120-129, (2018)
  • [8] Wang L., Ke J., Ye A., Wang W., Compromised accounts detection in Weibo based on multiple features, Journal of Wuhan University (Natural Science Edition), 66, 2, pp. 95-102, (2020)
  • [9] Wang Z., Ye W., Qiu X., Weibo abnormal account detect based on weighted Bayesian neural network, Computer and Digital Engineering, 46, 11, pp. 2323-2328, (2018)
  • [10] Xu H., Zhang H., He W., A cloud user anomaly detection method based on mouse behavior, Journal of Harbin University of Science and Technology, 24, 4, pp. 127-132, (2019)
12