文章基本信息

标题：SYBIL IDENTIFICATION IN SOCIAL NETWORKS USING SICT AND SICTF ALGORITHMS WITH IMPROVED KD-TREE
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作者：RENUGA DEVI. R ; M. HEMALATHA
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2013
卷号：56
期号：3
出版社：Journal of Theoretical and Applied
摘要：Most of the large scale social networking sites and small private social networks on the Internet are open to Sybil attacks. Lack in powerful user identity yields these systems at risk to Sybil attacks. A large number of methods have been proposed to solve this problem, but each method differs greatly from other based on the algorithms which they used, and network. In this paper we proposed two novel algorithms to identify the Sybil nodes in network community. We proposed SICT (Sybil identification using connectivity threshold) algorithm with Improved KD-Tree. Connections between the nodes are established, and connection threshold is compared with each node, if the connection establishment is exceeding the threshold then the node is identified as Sybil. We proposed SICTF (Sybil identification using connectivity threshold and frequency of visit or hitting the neighbors) algorithm, where the maximum variance of connectivity, length and frequency of a node can be calculated for a particular time interval and the maximum variance with respect to connectivity, length, and frequency is said to be Sybil. Both the algorithms are combined with previous Improved KD-Tree algorithm for community mining. Experimental results show that proposed SICTF algorithm performs well compared to the existing algorithm.
关键词：Social Networks; Sybil Node; Community Mining; Improved KD-Tree; SICT; SICTF