Analyzing spammers' social networks for fun and profit: a case study of cyber criminal ecosystem on twitter

In this paper, we perform an empirical analysis of the cyber criminal ecosystem on Twitter. Essentially, through analyzing inner social relationships in the criminal account community, we find that criminal accounts tend to be socially connected, forming a small-world network. We also find that criminal hubs, sitting in the center of the social graph, are more inclined to follow criminal accounts. Through analyzing outer social relationships between criminal accounts and their social friends outside the criminal account community, we reveal three categories of accounts that have close friendships with criminal accounts. Through these analyses, we provide a novel and effective criminal account inference algorithm by exploiting criminal accounts' social relationships and semantic coordinations.

[1]  Gerard Salton,et al.  Term-Weighting Approaches in Automatic Text Retrieval , 1988, Inf. Process. Manag..

[2]  M. KleinbergJon Authoritative sources in a hyperlinked environment , 1999 .

[3]  Andrew W. Moore,et al.  X-means: Extending K-means with Efficient Estimation of the Number of Clusters , 2000, ICML.

[4]  Michael Kaminsky,et al.  SybilGuard: defending against sybil attacks via social networks , 2006, SIGCOMM.

[5]  Georgia Koutrika,et al.  Combating spam in tagging systems , 2007, AIRWeb '07.

[6]  Phillip A. Porras,et al.  Highly Predictive Blacklisting , 2008, USENIX Security Symposium.

[7]  Virgílio A. F. Almeida,et al.  Identifying video spammers in online social networks , 2008, AIRWeb '08.

[8]  Virgílio A. F. Almeida,et al.  Detecting Spammers and Content Promoters in Online Video Social Networks , 2009, IEEE INFOCOM Workshops 2009.

[9]  Danah Boyd,et al.  Detecting Spam in a Twitter Network , 2009, First Monday.

[10]  Wolfgang Kellerer,et al.  Outtweeting the Twitterers - Predicting Information Cascades in Microblogs , 2010, WOSN.

[11]  Krishna P. Gummadi,et al.  Measuring User Influence in Twitter: The Million Follower Fallacy , 2010, ICWSM.

[12]  Eni Mustafaraj,et al.  From Obscurity to Prominence in Minutes: Political Speech and Real-Time Search , 2010 .

[13]  Kyumin Lee,et al.  Uncovering social spammers: social honeypots + machine learning , 2010, SIGIR.

[14]  Vern Paxson,et al.  @spam: the underground on 140 characters or less , 2010, CCS '10.

[15]  Jun Hu,et al.  Detecting and characterizing social spam campaigns , 2010, IMC '10.

[16]  Alex Hai Wang,et al.  Don't follow me: Spam detection in Twitter , 2010, 2010 International Conference on Security and Cryptography (SECRYPT).

[17]  Hosung Park,et al.  What is Twitter, a social network or a news media? , 2010, WWW '10.

[18]  Virgílio A. F. Almeida,et al.  Detecting Spammers on Twitter , 2010 .

[19]  ZhangZengbin,et al.  Unbiased sampling in directed social graph , 2010 .

[20]  Jacob Ratkiewicz,et al.  Detecting and Tracking the Spread of Astroturf Memes in Microblog Streams , 2010, ArXiv.

[21]  Gianluca Stringhini,et al.  Detecting spammers on social networks , 2010, ACSAC '10.

[22]  Barbara Poblete,et al.  Information credibility on twitter , 2011, WWW.

[23]  Chandra Prakash,et al.  SybilInfer: Detecting Sybil Nodes using Social Networks , 2011 .

[24]  Jacob Ratkiewicz,et al.  Truthy: mapping the spread of astroturf in microblog streams , 2010, WWW.

[25]  Chao Yang,et al.  Empirical Evaluation and New Design for Fighting Evolving Twitter Spammers , 2011, IEEE Transactions on Information Forensics and Security.