Journal
JOURNAL OF COMMUNICATIONS AND NETWORKS
Volume 17, Issue 2, Pages 145-156Publisher
KOREAN INST COMMUNICATIONS SCIENCES (K I C S)
DOI: 10.1109/JCN.2015.000028
Keywords
Link analysis; recommender systems; robust algorithm; Sybil attack
Funding
- National Research Foundation of Korea (NRF) - Ministry of Science, ICT & Future Planning [NRF-2014R1A1A1003562]
- National Research Foundation of Korea [2014R1A1A1003562] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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In recent years, electronic commerce and online social networks (OSNs) have experienced fast growth, and as a result, recommendation systems (RSs) have become extremely common. Accuracy and robustness are important performance indexes that characterize customized information or suggestions provided by RSs. However, nefarious users may be present, and they can distort information within the RSs by creating fake identities (Sybils). Although prior research has attempted to mitigate the negative impact of Sybils, the presence of these fake identities remains an unsolved problem. In this paper, we introduce a new weighted link analysis and influence level for RSs resistant to Sybil attacks. Our approach is validated through simulations of a broad range of attacks, and it is found to outperform other state-of-the-art recommendation methods in terms of both accuracy and robustness.
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