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

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. Copyright © 2015 Springer International Publishing Switzerland.