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Of Strategies and Structures: Motif-based Fingerprinting Analysis of Online Reputation Networks

Wichtlhuber, Matthias and Bücker, Sebastian and Kluge, Roland and Mousavi, Mahdi and Hausheer, David
Toelle, Jens and Akkaya, Kemal (eds.) :

Of Strategies and Structures: Motif-based Fingerprinting Analysis of Online Reputation Networks.
[Online-Edition: http://ieeexplore.ieee.org/document/7796822/]
Proceedings of the IEEE Conference on Local Computer Networks (LCN 2016)
[Conference or Workshop Item] , (2016)

Official URL: http://ieeexplore.ieee.org/document/7796822/

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.

Item Type: Conference or Workshop Item
Erschienen: 2016
Editors: Toelle, Jens and Akkaya, Kemal
Creators: Wichtlhuber, Matthias and Bücker, Sebastian and Kluge, Roland and Mousavi, Mahdi and Hausheer, David
Title: Of Strategies and Structures: Motif-based Fingerprinting Analysis of Online Reputation Networks
Language: English
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.

Title of Book: Proceedings of the IEEE Conference on Local Computer Networks (LCN 2016)
Divisions: 18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institute of Computer Engineering > Real-Time Systems
18 Department of Electrical Engineering and Information Technology > Institute of Computer Engineering
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Communications Engineering
18 Department of Electrical Engineering and Information Technology > Institute of Computer Engineering > Peer-to-Peer Systems Engineering
DFG-Collaborative Research Centres (incl. Transregio)
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > A: Construction Methodology
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > A: Construction Methodology > Subproject A1: Modelling
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > B: Adaptation Mechanisms
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > B: Adaptation Mechanisms > Subproject B3: Economics of Adaption
Event Location: Dubai, UAE
Event Dates: 2016-11-07 - 2016-11-10
Date Deposited: 25 Oct 2016 07:47
Official URL: http://ieeexplore.ieee.org/document/7796822/
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