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EXISTING SYSTEM

PROPOSED SYSTEM

EXISTING CONCEPT:-

Sybil detection mechanisms rely on the assumption that the benign region is fast mixing. we recast the problem of finding Sybil users as a semi-supervised learning problem, Sybil detection methods decrease dramatically when the benign region consists of more and more communities they cannot tolerate noise in their prior knowledge about known benign or Sybil nodes  and  they are not scalable.

PROPOSED CONCEPT:-

Sybil ranking mechanism achieves reasonably good performance. In their experimental evaluation, the authors found that using simple local community detection had equivalent results to using the state-of-art Sybil detection approaches. Several approaches have been proposed to propagate trust scores or reputation scores in file-sharing networks or auction platforms.

EXISTING ALGORITHM:-

Iterative algorithm

PROPOSED ALGORITHM:-

Loopy Belief Propagation

ALGORITHM DEFINITION:-

An iterative method is a mathematical procedure that generates a sequence of improving approximate solutions for a class of problems. A specific implementation of an iterative method, including the termination criteria, is an algorithm of the iterative method. 

ALGORITHM DEFINITION:-

Belief propagation algorithm exists for several types of graphical models Bayesian network and markov random field in particular. We describe here the variant that operates on a factor graph.

DRAWBACK:-

They can bootstrap from either only known benign or known Sybil nodes limiting their detection accuracy.

They are not scalable.

ADVANTAGES:-

Edge devices are providing authenticated access to faster, more efficient backbone and core networks. 

The maximum number of accepted Sybil’s and the maximum number of rejected benign nodes for a given number of attack edges.