Online Social Networks (OSNs) have become extremely popular in recent years, leading to the presence of huge volumes of users' personal information on the Internet. This increases the need for efficient and effective measures helping users to judge their direct contacts so as to avoid friendship with malicious users that could misuse their personal information. At this purpose, in this paper we propose a risk measure, called local risk factor, having as a key idea the fact the malicious users in OSNs (aka attackers) show some common features on the topology of their social graphs, which is different from those of legitimate users. This consideration brought us to design a set of features defined based on attacker activity patterns. To prove the effectiveness of the proposed risk measure, we run several experiments on a real OSN dataset (i.e., Orkut social network) with more than 3 million vertices and 117 million edges, by injecting synthetic fake users according to different settings and showing how the proposed measures can indeed help in their detection.

Graph based local risk estimation in large scale online social networks

LALEH, NAEIMEH;CARMINATI, BARBARA;FERRARI, ELENA
2015-01-01

Abstract

Online Social Networks (OSNs) have become extremely popular in recent years, leading to the presence of huge volumes of users' personal information on the Internet. This increases the need for efficient and effective measures helping users to judge their direct contacts so as to avoid friendship with malicious users that could misuse their personal information. At this purpose, in this paper we propose a risk measure, called local risk factor, having as a key idea the fact the malicious users in OSNs (aka attackers) show some common features on the topology of their social graphs, which is different from those of legitimate users. This consideration brought us to design a set of features defined based on attacker activity patterns. To prove the effectiveness of the proposed risk measure, we run several experiments on a real OSN dataset (i.e., Orkut social network) with more than 3 million vertices and 117 million edges, by injecting synthetic fake users according to different settings and showing how the proposed measures can indeed help in their detection.
2015
Proceedings - 2015 IEEE International Conference on Smart City, SmartCity 2015, Held Jointly with 8th IEEE International Conference on Social Computing and Networking, SocialCom 2015, 5th IEEE International Conference on Sustainable Computing and Communications, SustainCom 2015, 2015 International Conference on Big Data Intelligence and Computing, DataCom 2015, 5th International Symposium on Cloud and Service Computing, SC2 2015
9781509018932
IEEE International Conference on Smart City, SmartCity 2015
chn
2015
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11383/2062599
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