Detecting Spam albums in online social network

Shao-qing LYU; Yu-qing ZHANG; Dong-hang LIU; Guang-hua ZHANG

doi:10.11959/j.issn.1000-436x.2016180

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Detecting Spam albums in online social network

Papers | 更新时间：2024-06-05

- Detecting Spam albums in online social network
- Journal on Communications Vol. 37, Issue 9, Pages: 75-91(2016)
- 作者机构：
  
  1. 西安电子科技大学综合业务网理论及关键技术国家重点实验室，陕西西安710071
  2. 中国科学院大学国家计算机网络入侵防范中心，北京 100190
  3. 中国科学院信息工程研究所物联网信息安全技术北京市重点实验室,北京 100097
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61572460);The National Natural Science Foundation of China(61272481);The National Natural Science Foundation of China(61303239);Open Fund of Beijing Key Laboratory of IOT Information Security Technology;China Postdoctoral Science Foundation(2015M582622)
- DOI：10.11959/j.issn.1000-436x.2016180
  CLC： TP393
- Online First：2016-09，
  
  Published：25 September 2016
- 稿件说明：
移动端阅览
Shao-qing LYU, Yu-qing ZHANG, Dong-hang LIU, et al. Detecting Spam albums in online social network[J]. Journal on Communications, 2016, 37(9): 75-91.
DOI：

Shao-qing LYU, Yu-qing ZHANG, Dong-hang LIU, et al. Detecting Spam albums in online social network[J]. Journal on Communications, 2016, 37(9): 75-91. DOI： 10.11959/j.issn.1000-436x.2016180.

摘要

提出一种针对Spam相册的检测方案。首先分析了Photo Spam的攻击特点以及与传统Spam的差异，在此基础上构造了12个提取及时且计算高效的特征。利用这些特征提出了有监督学习的检测模型，通过2 356个相册的训练形成Spam相册分类器，实验表明能够正确检测到测试集中100%的Spam相册和98.2%的正常相册。最后将训练后的模型应用到包含315 115个相册的真实数据集中，检测到89 163个Spam相册，正确率达到97.2%。

Abstract

A supervised learning solution to detect Spam albums instead of spammers in Photo Spam was proposed.Specifically

the characteristics of Photo Spam and the differences between Photo Spam and traditional Spam were analyzed.Then 12 features which were extracted easily and calculated efficiently were constructed based on the analysis.Next a classification model was built with a dataset of 2 356 labeled albums to identify Spam albums.The model provided excellent performance with true positive rates of Spam albums and normal albums

reaching 100% and 98.2% respectively.Finally

the detection model were applied to 315 115 unlabeled albums and detected 89 163 spam albums with a true positive rate of 97.2%.

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references

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