Mining the Association of Multiple Virtual Identities Based on Multi-Agent Interaction

Abuses of online anonymity make identity tracing a critical problem in cybercrime investigation. To solve this problem, this paper focuses on the feature of authors’ behavior in time slices and tries to mine the association of multiple virtual identities based on multi-agent interaction. We propose the recognition model MVIA-K based on knowledge management. In MVIA-K, agents perform distributed mining to get candidate author groups as local knowledge in each time slice. Then high-quality knowledge is extracted from the local knowledge and used as priori knowledge to guide other agents’ mining process. Finally distributed knowledge is integrated on the basis of knowledge scale. Experiment with real-world dataset shows that MVIA-K has a very promising performance, which can filter the noise data effectively and outperform Author Topic model.

[1]  Danielle S. McNamara,et al.  Handbook of latent semantic analysis , 2007 .

[2]  Thomas L. Griffiths,et al.  Probabilistic author-topic models for information discovery , 2004, KDD.

[3]  Audun Jøsang,et al.  A survey of trust and reputation systems for online service provision , 2007, Decis. Support Syst..

[4]  Juan-Zi Li,et al.  Exploiting Temporal Authors Interests via Temporal-Author-Topic Modeling , 2009, ADMA.

[5]  E. Airoldi,et al.  Data Mining Challenges for Electronic Safety: The Case of Fraudulent Intent Detection in E-Mails , 2004 .

[6]  Rong Zheng,et al.  From fingerprint to writeprint , 2006, Commun. ACM.

[7]  Srinivasan Venkatesh,et al.  Battling the Internet water army: Detection of hidden paid posters , 2011, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).

[8]  Efstathios Stamatatos.  A survey of modern authorship attribution methods , 2009 .

[9]  Hsinchun Chen,et al.  Writeprints: A stylometric approach to identity-level identification and similarity detection in cyberspace , 2008, TOIS.

[10]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

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

[12]  Weidong Xiao,et al.  The Recognition of Multiple Virtual Identities Association Based on Multi-agent System , 2013, ADMI.

[13]  Thomas L. Griffiths,et al.  The Author-Topic Model for Authors and Documents , 2004, UAI.

[14]  Thomas L. Griffiths,et al.  Probabilistic Topic Models , 2007 .