Malicious Peers Detection Framework for Peer-to-Peer Systems
Xianglin Wei, Jianhua Fan, Tongxiang Wang, Qiping Wang
Journal of Digital Information Management, Vol. 13, No. 4, 2015.
DOI: 
Link: http://dline.info/fpaper/jdim/v13i4/v13i4_3.pdf
Abstract:
A number of reputation mechanisms are
introduced in recent years to alleviate the blindness during
peer selection in distributed P2P environment where
malicious peers coexist with honest ones. They indeed
provide incentives for peers to contribute more resources
to the system, and thus, promote the whole system
performance. However, little attention has been paid on
how to identify the malicious peers in this situation. In
this paper, a general framework is presented for detecting
malicious peers in Reputation-based P2P systems. Firstly,
the malicious peers are divided into various categories
and the problem is formulated. Secondly, the general
framework is put forward which mainly contains four steps,
i.e. data collection, data processing, malicious peers
detection and malicious peers clustering. Thirdly, an
algorithm implementation of this general framework
named IDEA is put forward based on Principal Direction
Divisive Partitioning and K-means clustering. Finally, a
series of simulation experiments are conducted to evaluate
the effectiveness of IDEA. Simulation results have shown
that IDEA can precisely distinguish and cluster the
malicious peers.