Neighbor similarity trust against Sybil attack in P2P E-commerce

Guojun Wang, Felix Musau, Song Guo, Muhammad Bashir Abdullahi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

36 Citations (Scopus)

Abstract

Peer to peer (P2P) e-commerce applications exist at the edge of the Internet with vulnerabilities to passive and active attacks. These attacks have pushed away potential business firms and individuals whose aim is to get the best 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 address Sybil attack, an active attack, in which peers can have bogus and multiple identities to fake their owns. Most existing work, which concentrates on social networks and trusted certification, has not been able to prevent Sybil attack peers from doing transactions. Our work exploits the neighbor similarity trust relationship to address Sybil attack. In our approach, duplicated Sybil attack peers can be identified as the neighbor peers become acquainted and hence more trusted to each other. Security and performance analysis shows that Sybil attack can be minimized by our proposed neighbor similarity trust.
Original languageEnglish
Article number6776524
Pages (from-to)824-833
Number of pages10
JournalIEEE Transactions on Parallel and Distributed Systems
Volume26
Issue number3
DOIs
Publication statusPublished - 1 Mar 2015
Externally publishedYes

Keywords

  • Collusion attack
  • Neighbor similarity
  • P2P
  • Sybil attack
  • Trust

ASJC Scopus subject areas

  • Signal Processing
  • Hardware and Architecture
  • Computational Theory and Mathematics

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