在线社交网络中Spam相册检测方案

吕少卿; 张玉清; 刘东航; 张光华

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

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在线社交网络中Spam相册检测方案

学术论文 | 更新时间：2024-06-05

- 在线社交网络中Spam相册检测方案
- Detecting Spam albums in online social network
- 通信学报 2016年37卷第9期页码：75-91
- 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：
  
  [ "吕少卿（1987-），男，山西五寨人，西安电子科技大学博士生，主要研究方向为在线社交网络安全。" ]
  [ "张玉清（1966-），男，陕西宝鸡人，博士，中国科学院大学教授、博士生导师，主要研究方向为网络与信息系统安全。" ]
  [ "刘东航（1990-），男，山西太原人，西安电子科技大学硕士生，主要研究方向为网络和信息安全。" ]
  [ "张光华（1979-），男，河北石家庄人，博士，西安电子科技大学博士后在站，主要研究方向为信任管理、无线网络安全。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61572460);国家自然科学基金资助项目(61272481);国家自然科学基金资助项目(61303239);物联网信息安全技术北京市重点实验室开放课题基金资助项目;中国博士后科学基金资助项目(2015M582622)
- DOI：10.11959/j.issn.1000-436x.2016180
  中图分类号： TP393
- 网络首发：2016-09，
  
  纸质出版：2016-09-25
- 稿件说明：
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吕少卿, 张玉清, 刘东航, 等. 在线社交网络中Spam相册检测方案[J]. 通信学报, 2016,37(9):75-91.

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.
吕少卿, 张玉清, 刘东航, 等. 在线社交网络中Spam相册检测方案[J]. 通信学报, 2016,37(9):75-91. DOI： 10.11959/j.issn.1000-436x.2016180.

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%.

关键词

Keywords

references

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