A Relationship based Collusive Attack Detection Mechanism for Reputation Aggregation in Social Network
- DOI
- 10.2991/meic-14.2014.227How to use a DOI?
- Keywords
- Collusive attack detection; Reputation Aggregation; Relationship; Social Network; Collusion factor
- Abstract
Reputation aggregation is a significant and inevitable mechanism for ensuring the security in social network. To solve the problem of preventing collusive attack in reputation aggregation in social network, a collusive attack detection mechanism (CADM) is proposed based on users’ relationships and their judgment evaluation. Firstly, the rationales of CADM include evaluations of inauthentic judgment, attack behavior similarity, similar reputation of colluders, and the close trust relationship among colluders. The construction of CADM includes four parts as social graph, trust schedule, reputation aggregation form, and collusive factor. Secondly, the four detail collusive factors, including item judgment factor, user similar factor, trust relationship factor and user malicious factor, are addressed respectively to evaluate the probability of collusion happening. And finally, a trust relationship based detection process of CADM, which is comprised by three aspects as attack happening evaluation, user detection, and relationship traversing, is present to find collusive attack through the social relationships in SNS.
- Copyright
- © 2014, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Bo Zhang AU - Hailei Yuan AU - Feng Song AU - Hao Li PY - 2014/11 DA - 2014/11 TI - A Relationship based Collusive Attack Detection Mechanism for Reputation Aggregation in Social Network BT - Proceedings of the 2014 International Conference on Mechatronics, Electronic, Industrial and Control Engineering PB - Atlantis Press SP - 1016 EP - 1019 SN - 2352-5401 UR - https://doi.org/10.2991/meic-14.2014.227 DO - 10.2991/meic-14.2014.227 ID - Zhang2014/11 ER -