Spectrum based fraud detection in social networks
Social networks are vulnerable to various attacks such as spam emails, viral marketing and the such. In this paper we develop a spectrum based detection framework to discover the perpetrators of these attacks. In particular, we focus on Random Link Attacks (RLAs) in which the malicious user creates...
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| creator | Ying, Xiaowei Wu, Xintao Barbara, Daniel |
| description | Social networks are vulnerable to various attacks such as spam emails, viral marketing and the such. In this paper we develop a spectrum based detection framework to discover the perpetrators of these attacks. In particular, we focus on Random Link Attacks (RLAs) in which the malicious user creates multiple false identities and interactions among those identities to later proceed to attack the regular members of the network. We show that RLA attackers can be filtered by using their spectral coordinate characteristics, which are hard to hide even after the efforts by the attackers of resembling as much as possible the rest of the network. Experimental results show that our technique is very effective in detecting those attackers and outperforms techniques previously published. |
| doi_str_mv | 10.1109/ICDE.2011.5767910 |
| format | Conference Proceeding |
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In this paper we develop a spectrum based detection framework to discover the perpetrators of these attacks. In particular, we focus on Random Link Attacks (RLAs) in which the malicious user creates multiple false identities and interactions among those identities to later proceed to attack the regular members of the network. We show that RLA attackers can be filtered by using their spectral coordinate characteristics, which are hard to hide even after the efforts by the attackers of resembling as much as possible the rest of the network. Experimental results show that our technique is very effective in detecting those attackers and outperforms techniques previously published.</abstract><pub>IEEE</pub><doi>10.1109/ICDE.2011.5767910</doi><tpages>12</tpages></addata></record> |
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| identifier | ISSN: 1063-6382 |
| ispartof | 2011 IEEE 27th International Conference on Data Engineering, 2011, p.912-923 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Approximation methods Blogs Collaboration Eigenvalues and eigenfunctions Electronic mail Social network services Topology |
| title | Spectrum based fraud detection in social networks |
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