软件导刊2025，Vol.24Issue(2)：98-106,9.DOI:10.11907/rjdk.241006
基于Gini-PSO-DPC算法的物联网数据异常检测
Anomaly Detection of IoT Data Based on Gini-PSO-DPC Algorithm
摘要
Abstract
With the massive growth of IoT devices,the data they generate is also growing exponentially.Data only has value when it has ac-ceptable quality,and noisy data is inevitable in massive amounts of data.A density peak clustering algorithm called Gini PSO-DPC based on Gini coefficient and particle swarm optimization algorithm is proposed to address this issue.Firstly,the optimal cutoff distance is calculated us-ing the Gini coefficient based on all data points;Secondly,the particle swarm optimization algorithm is used to find K approximately optimal initial cluster centers and generate K initial category clusters;Finally,the sample data points are assigned to the corresponding category clus-ter based on the density of the nearest data point's category.The simulation experiment results show that the average accuracy of the Gini PSO-DPC algorithm reaches 96.81%,which is 2.44%,0.89%,and 0.9%higher than the improved K-means,DMGA-FCM,and DPC algorithms,respectively;The average accuracy reached 94.3%,which was 1.22%,2.02%,and 1.33%higher than the improved K-means,DMGA-FCM,and DPC algorithms,respectively.In the ablation experiment,the Gini PSO-DPC algorithm showed a more stable and reasonable cut-off distance parameter setting,shorter clustering time,indicating that the algorithm has stronger global search ability,higher adaptability,and better clustering effect.关键词
物联网/聚类算法/DPC/Gini-PSO-DPC/异常检测Key words
Internet of Things/clustering algorithm/DPC/Gini-PSO-DPC/anomaly detection分类
信息技术与安全科学引用本文复制引用
罗碧,沈艳..基于Gini-PSO-DPC算法的物联网数据异常检测[J].软件导刊,2025,24(2):98-106,9.基金项目
国家自然科学基金项目(6217206) （6217206）
四川省科技计划重点研发项目(2023YFG0116) （2023YFG0116）
