Intelligent Sybil attack detection on abnormal connectivity behavior in mobile social networks

被引：0

作者：

Chinchore, Anand ^{[1
]}

Jiang, Frank ^{[1
]}

Xu, Guandong ^{[1
]}

机构：

[1] Advanced Analytics Institute, University of Technology Sydney, Ultimo

来源：

Lecture Notes in Business Information Processing | 2015年 / 224卷

关键词：

Anomaly detection; Mobile social network; Sybil attack;

D O I：

10.1007/978-3-319-21009-4_45

中图分类号：

学科分类号：

摘要：

There have been a large number of researches on mobile networks in the literature, focusing on a variety of secured applications over the network, including the use of their connections, fake identification and attacks on social group. These applications are created for the intention to collect confidential information, money laundering, blackmailing and to perform other crime activity. The purpose of this research is to identify the behavior of the honest node (network account) and fake node (network account) on mobile social network. In this research, the behavior survey of these nodes is carried out and further analysed with the help of graph-based Sybil detection system. This paper particularly studies Sybil attacks and its defense system for IoT (Internet-of-Things) environment. To be implied, the identification of each forged Sybil node is to be tracked on the basis of nodes connectivity and their timing of connectivity as well as frequency among each other. Sybil node has a forged identity in different locations and also reports its virtual location information to servers. © Springer International Publishing Switzerland 2015.

引用

页码：602 / 617

页数：15

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