A New Approach to Detect User Collusion Behavior in Online QA System

DOI

10.2991/cnct-16.2017.116How to use a DOI?

Keywords

Collusion, Sybil Detection, Interaction Weight, Clustering Coefficient

Abstract

Sybils and Sybil attacks are problems born with social networks. In online QA application Afanti, Sybil users collude with each other to mimic normal users and lower the possibility to be detected. In this paper, we stated this phenomenon and put forward a new approach that can detect user collusion behavior which cannot be detected before. In this approach, we defined interaction weight between users to describe the collusion, clustered users by this weight and labeled users as Sybil or normal through clustering coefficient. We also proposed a plan for deploying this approach in large-scale system and pointed out the key part and how to improve it. The experiment shows the accuracy of our approach is93.5% in detecting Sybil questioners and 97.4% in Sybil answerers. Our approachcan also recall many Sybils which cannot be detected by the original detection system.

Copyright

© 2017, 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/).

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TY  - CONF
AU  - Zhen-hui ZHU
AU  - Zhi YANG
AU  - Ya-fei DAI
PY  - 2016/12
DA  - 2016/12
TI  - A New Approach to Detect User Collusion Behavior in Online QA System
BT  - Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)
PB  - Atlantis Press
SP  - 836
EP  - 842
SN  - 2352-538X
UR  - https://doi.org/10.2991/cnct-16.2017.116
DO  - 10.2991/cnct-16.2017.116
ID  - ZHU2016/12
ER  -