Journal on Communications ›› 2016, Vol. 37 ›› Issue (9): 75-91.doi: 10.11959/j.issn.1000-436x.2016180

• Papers • Previous Articles     Next Articles

Detecting Spam albums in online social network

Shao-qing LYU1,Yu-qing ZHANG1,2,Dong-hang LIU1,Guang-hua ZHANG1,3   

  1. 1 Information Security Research Center of State Key Laboratory of Integrated Services Networks,Xidian University,Xi'an 710071,China
    2 National Computer Network Intrusion Protection Center,University of Chinese Academy of Sciences,Beijing 100190,China
    3 Beijing Key Laboratory of IOT Information Security Technology,Institute of Information Engineering,CAS,Beijing 100097,China
  • Online:2016-09-25 Published:2016-09-28
  • Supported by:
    The National Natural Science Foundation of China;The National Natural Science Foundation of China;The National Natural Science Foundation of China;Open Fund of Beijing Key Laboratory of IOT Information Security Technology;China Postdoctoral Science Foundation

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%.

Key words: social network security, Photo Spam, Spam detection, RenRen

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