Title
Of Strategies and Structures: Motif-Based Fingerprinting Analysis of Online Reputation Networks
Abstract
Reputation networks are an important building block of distributed systems whenever reliability of nodes is an issue. However, reputation ratings can easily be undercut: colluding nodes can spread good ratings for each other while third parties are hardly able to detect the fraud. There is strong analytical evidence that reputation networks cannot be constructed in a way to guarantee security. Consequently, only statistical approaches are promising. This work pursues a statistical approach inspired by the idea that colluding node's behavior changes the local structure of a reputation network. To measure these structural changes, we extend a graph analysis method originating from molecular biology and combine it with a machine learning approach to analyze fingerprints of node's interactions. We evaluate our method using an adaptive Peer-to-Peer (P2P) streaming system and show that a correct classification of up to 98% is possible.
Year
DOI
Venue
2016
10.1109/LCN.2016.76
2016 IEEE 41st Conference on Local Computer Networks (LCN)
Keywords
Field
DocType
motif-based fingerprinting analysis,online reputation network,distributed system,statistical approach,graph analysis,node interaction,peer-to-peer streaming system,P2P streaming system
Data mining,Adaptive system,Computer science,Support vector machine,Local structure,Computer network,Feature extraction,Power graph analysis,Motif (music),Bandwidth (signal processing),Reputation
Conference
ISSN
ISBN
Citations 
0742-1303
978-1-5090-2055-3
0
PageRank 
References 
Authors
0.34
13
5
Name
Order
Citations
PageRank
Matthias Wichtlhuber1626.23
Sebastian Bucker200.34
Roland Kluge3235.06
Mahdi Mousavi493.21
David Hausheer540249.15