Identification of Sybil Attack on Social Networks

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2016 by IJCTT Journal
Volume-34 Number-2
Year of Publication : 2016
Authors : Dr.J.I.Sheeba, V.Saranya, Dr.S.Pradeep Devaneyan
  10.14445/22312803/IJCTT-V34P112

MLA

Dr.J.I.Sheeba, V.Saranya, Dr.S.Pradeep Devaneyan "Identification of Sybil Attack on Social Networks". International Journal of Computer Trends and Technology (IJCTT) V34(2):68-74, April 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Social networks play a vital role in daily life since it is vulnerable to many security attacks, namely Sybil attacks. The Sybil attack is an attack where a single user can create a many bogus identity to impersonate like others. The Sybil node in social networking is used for criminal activities such as stealing legitimate information about the user present in social networks it will lead to system degradation process. Since the Sybil identification algorithm does not provide a complete solution to detect the Sybil node in social networks. In order to overcome these drawbacks Sybil defender algorithm gets deployed in a proposed framework to detect the Sybil node in social networks. The Sybil defender algorithm will perform a limited number of random walk on social networks. Sybil defender is a combination of both Sybil identification algorithm and Sybil community detection algorithm. A Sybil identification algorithm is used to detect the Sybil node and Sybil community detection algorithm is used to detect Sybil community around the Sybil node in the social networks. By comparing with the existing approaches Sybil node will be effectively detected using Sybil defender algorithm. This proposed and existing works are measured in terms of evaluation metrics namely non-trustworthy rate, detection rate, packet loss, end to end delivery.

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Keywords
Sybil attack, Sybil identification algorithm, Sybil defender algorithm, Social networks, Sybil community detection algorithm.