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PROJECT TITLE:

Neighbor Similarity Trust against Sybil Attack in P2P E-Commerce - 2015

ABSTRACT:

Peer to look (P2P) e-commerce applications exist at the sting of the Internet with vulnerabilities to passive and active attacks. These attacks have pushed away potential business firms and people whose aim is to get the most effective benefit in e-commerce with minimal losses. The attacks occur during interactions between the trading peers as a transaction takes place. In this paper, we propose how to handle Sybil attack, a lively attack, in which peers will have bogus and multiple identities to fake their owns. Most existing work, that concentrates on social networks and trusted certification, has not been ready to prevent Sybil attack peers from doing transactions. Our work exploits the neighbor similarity trust relationship to handle Sybil attack. In our approach, duplicated Sybil attack peers can be identified because the neighbor peers become acquainted and hence a lot of trusted to every other. Security and performance analysis shows that Sybil attack will be minimized by our proposed neighbor similarity trust.


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