TU Darmstadt / ULB / TUbiblio

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

Wichtlhuber, Matthias ; Bücker, Sebastian ; Kluge, Roland ; Mousavi, Mahdi ; Hausheer, David
Toelle, Jens ; 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)
[Konferenz- oder Workshop-Beitrag], (2016)

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

Kurzbeschreibung (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.

Typ des Eintrags: Konferenz- oder Workshop-Beitrag (Keine Angabe)
Erschienen: 2016
Herausgeber: Toelle, Jens ; Akkaya, Kemal
Autor(en): Wichtlhuber, Matthias ; Bücker, Sebastian ; Kluge, Roland ; Mousavi, Mahdi ; Hausheer, David
Titel: Of Strategies and Structures: Motif-based Fingerprinting Analysis of Online Reputation Networks
Sprache: Englisch
Kurzbeschreibung (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.

Buchtitel: Proceedings of the IEEE Conference on Local Computer Networks (LCN 2016)
Fachbereich(e)/-gebiet(e): 18 Fachbereich Elektrotechnik und Informationstechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Datentechnik > Echtzeitsysteme
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Datentechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Kommunikationstechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Datentechnik > Entwurfsmethodik für Peer-to-Peer Systeme
DFG-Sonderforschungsbereiche (inkl. Transregio)
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet > A: Konstruktionsmethodik
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet > A: Konstruktionsmethodik > Teilprojekt A1: Modellierung
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet > B: Adaptionsmechanismen
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet > B: Adaptionsmechanismen > Teilprojekt B3: Adaptionsökonomie
Veranstaltungsort: Dubai, UAE
Veranstaltungsdatum: 2016-11-07 - 2016-11-10
Hinterlegungsdatum: 25 Okt 2016 07:47
Offizielle URL: http://ieeexplore.ieee.org/document/7796822/
Verwandte URLs:
Export:

Optionen (nur für Redakteure)

Eintrag anzeigen Eintrag anzeigen