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, which is a kind of active attack. The peers can have bogus and multiple identity to fake their own ones. Most existing work, which concentrates on social networks and trusted certification, has not been able to prevent Sybil attack peers from participating in transactions. Our work exploits the neighbor similarity trust relationship to address Sybil attack. In this approach, referred to as SybilTrust, duplicated Sybil attack peers can be recognized as the neighbor peers become acquainted and hence more trusted to each other. Security and performance analysis shows Sybil attack can be minimized by our proposed neighbor similarity trust.
Original language | English |
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Title of host publication | Proceedings - IEEE 9th International Conference on Ubiquitous Intelligence and Computing and IEEE 9th International Conference on Autonomic and Trusted Computing, UIC-ATC 2012 |
Pages | 547-554 |
Number of pages | 8 |
DOIs | |
Publication status | Published - 28 Nov 2012 |
Externally published | Yes |
Event | 9th IEEE International Conference on Ubiquitous Intelligence and Computing, UIC 2012 and 9th IEEE International Conference on Autonomic and Trusted Computing, ATC 2012 - Fukuoka, Japan Duration: 4 Sept 2012 → 7 Sept 2012 |
Conference
Conference | 9th IEEE International Conference on Ubiquitous Intelligence and Computing, UIC 2012 and 9th IEEE International Conference on Autonomic and Trusted Computing, ATC 2012 |
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Country/Territory | Japan |
City | Fukuoka |
Period | 4/09/12 → 7/09/12 |
Keywords
- Collusion attack
- Neighbor similarity.
- P2P
- Sybil attack
- Trust
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications