计算机工程

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基于传播时空特性的社交网络灰帽用户检测

  

  • 发布日期:2020-12-09

Grey Hat User Detection in Social Network Based on Spatiotemporal Characteristics of Diffusion

  • Published:2020-12-09

摘要: 社交网络灰帽用户虽然种类多样又善于伪装,但因最终目的都是扩大自身影响力,故在交互行为上具有共同特性, 即在其用户生成内容(user generated content, UGC)或参与他人 UGC 传播过程中与白帽用户相比表现出明显差异。基于此,为 了解决社交网络灰帽用户因极易隐藏且类型多样导致的检测算法适用性低的问题,本文提出一种基于传播时空特性的社交网 络检测机制—DSTC(Diffusion Spatiotemporal Characteristics)。首先,挖掘社交网络白帽/灰帽用户各自的 UGC 在传播时序上的 不同特性;而后,通过构建 UGC 传播网络来度量白帽/灰帽用户在传播空间上的不同特性;基于挖掘出的传播时空特性,设 计了社交网络用户检测机制 DSTC,融合时空传播特性并调节权重比例提高分类性能。实验结果表明,与几类主流灰帽用户 检测算法相比,DSTC 在多个数据集上的 Accuracy 值最高提升 26.08%,AUC 值有 0.008%~30.54%的大幅提升,表明 DSTC 能有效检测不同类型潜藏灰帽用户。

Abstract: Although social network gray hat users are diverse and good at disguising, their ultimate goal is to expand their own influence, so they have common characteristics in their interactive behavior. That is, in their user generated content (UGC) or participate in the spread of UGC by others, there are obvious differences in the diffusion process compared with white hat users. Based on this, in order to solve the problem of low applicability of detection algorithms due to the easy concealment of social network gray hat users and their diverse types, this paper proposes a social network detection mechanism based on the spreading spatiotemporal characteristics of propagation—DSTC (Diffusion Spatiotemporal Characteristics). Firstly, exploring the different characteristics of the dissemination timing of the respective UGC of the white/gray hat users of social networks; then, by constructing a UGC dissemination network to measure the different characteristics of the white/gray hat users in the dissemination space; based on the dissemination discovered spatiotemporal characteristics, a social network user detection mechanism DSTC is designed, which integrates spatiotemporal propagation characteristics and adjusts the weight ratio to improve classification performance. Experimental results show that, compared with several mainstream gray hat user detection algorithms, the Accuracy value of DSTC on multiple data sets is increased by 26.08%, and the AUC value is greatly increased from 0.008% to 30.54%, indicating that DSTC can effectively detect different types of potential gray hat users.