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2009 International Conference on Advances in Social Network Analysis and Mining
A Method for Identifying Malicious Activity in Collaborative Systems with Maps
Athens, Greece
July 20-July 22
ISBN: 978-0-7695-3689-7
| ASCII Text | x | ||
| Vasco Furtado, Thiago Assunção, Marcos de Oliveira, Mairon Belchior, Jonathan D'Orleans, "A Method for Identifying Malicious Activity in Collaborative Systems with Maps," 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 334-337, 2009 International Conference on Advances in Social Network Analysis and Mining, 2009. | |||
| BibTex | x | ||
| @article{ 10.1109/ASONAM.2009.35, author = {Vasco Furtado and Thiago Assunção and Marcos de Oliveira and Mairon Belchior and Jonathan D'Orleans}, title = {A Method for Identifying Malicious Activity in Collaborative Systems with Maps}, journal ={2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining}, volume = {0}, year = {2009}, isbn = {978-0-7695-3689-7}, pages = {334-337}, doi = {http://doi.ieeecomputersociety.org/10.1109/ASONAM.2009.35}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining TI - A Method for Identifying Malicious Activity in Collaborative Systems with Maps SN - 978-0-7695-3689-7 SP334 EP337 A1 - Vasco Furtado, A1 - Thiago Assunção, A1 - Marcos de Oliveira, A1 - Mairon Belchior, A1 - Jonathan D'Orleans, PY - 2009 KW - social networks KW - data mining KW - wikimapps KW - colaborative systems VL - 0 JA - 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining ER - | |||
In this paper we describe the method we have created for the purpose of identifying misbehavior of users who intends to generate false trends in digital maps. Basically, the idea is to identify patterns of communities of users who strongly contribute with reports that lead a particular geographic area to be considered a hot spot. The association between hot spots, computed from Kernel Density Estimation techniques, and the methods for identifying communities in social networks is the main innovation of the method proposed. A multi-agent system was built in order to simulate several scenarios of malicious activities. This method has shown to be effective for alerting the possibility of malicious activity in a real system.
Index Terms:
social networks, data mining, wikimapps, colaborative systems
Citation:
Vasco Furtado, Thiago Assunção, Marcos de Oliveira, Mairon Belchior, Jonathan D'Orleans, "A Method for Identifying Malicious Activity in Collaborative Systems with Maps," asonam, pp.334-337, 2009 International Conference on Advances in Social Network Analysis and Mining, 2009
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