https:\/\/zoom.us\/j\/2445226856<\/a><\/p>\n\n\n\nMembres du jury :<\/strong><\/p>\n\n\n\nM. Noel CRESPI<\/strong>, Full professor, T\u00e9l\u00e9com SudParis, FRANCE – Directeur de these
M. Davide FREY<\/strong>, Charg\u00e9 de recherche, inria, FRANCE – Rapporteur
Mme Hanane EL BAKKALI<\/strong>, Full professor, ENSIAS, MAROC – Rapporteur
M. Abdellatif MEZRIOUI <\/strong>, Full professor, INPT (Institute National Des Postes And Telecommunications), MAROC – Directeur de these
Mme Thi-Mai-Trang NGUYEN<\/strong>, Full professor, Sorbonne Universit\u00e9 , FRANCE – Rapporteur
Mme Cigdem SANGUL<\/strong>, Assistant professor, Brunel University, ROYAUME-UNI – Examinateur<\/p>\n\n\n\n\u00ab S\u00e9curiser le partage de ressources P2P via la blockchain et les m\u00e9canismes de confiance et de r\u00e9putation bas\u00e9s sur les GNN (Graph Neural Networks). \u00bb<\/h2>\n\n\n\npr\u00e9sent\u00e9 par Monsieur Badr BELLAJ<\/h2>\n\n\n\n
R\u00e9sum\u00e9 :<\/strong><\/p>\n\n\n\nL’\u00e9mergence de la technologie blockchain et des cryptomonnaies a ouvert la possibilit\u00e9 de cr\u00e9er de nouveaux mod\u00e8les de gestion, de partage et de mon\u00e9tisation de ressources en pair-\u00e0-pair (P2P). \u00c9tant donn\u00e9 que ces mod\u00e8les P2P sont sans confiance (trustless), des m\u00e9canismes de confiance et de r\u00e9putation fiables et efficaces sont n\u00e9cessaires pour minimiser le risque d’acc\u00e8s ou d’interaction avec des pairs malveillants. Plusieurs syst\u00e8mes de gestion de confiance bas\u00e9s sur la r\u00e9putation (RTMS) ont \u00e9t\u00e9 propos\u00e9s pour garantir la confiance dans les r\u00e9seaux P2P, aider \u00e0 choisir des ressources fiables et emp\u00eacher les comportements malveillants des pairs. Ces RTMS \u00e9tablissent la confiance en s’appuyant sur des r\u00e9putations bas\u00e9es sur la communaut\u00e9. Ils aident les pairs \u00e0 \u00e9valuer la fiabilit\u00e9 des autres et \u00e0 \u00e9valuer la qualit\u00e9 de service (QoS) en fonction de leur r\u00e9putation et de leurs exp\u00e9riences pass\u00e9es mutuelles. Dans ces sch\u00e9mas, un r\u00e9seau de confiance en surcouche pair-\u00e0-pair est \u00e9tabli. Cette th\u00e8se pr\u00e9sente BTrust, un nouveau syst\u00e8me de gestion de confiance d\u00e9centralis\u00e9 et modulaire pour les r\u00e9seaux P2P \u00e0 grande \u00e9chelle, exploitant la technologie blockchain et les GNN (Graph Neural Network) pour l’\u00e9valuation de la confiance. BTrust introduit un mod\u00e8le de confiance et de r\u00e9putation multidimensionnel pour \u00e9valuer la fiabilit\u00e9 des pairs, d\u00e9rivant dynamiquement une valeur unique \u00e0 partir de plusieurs param\u00e8tres. La blockchain garantit un calcul, une diffusion et un stockage fiables de la confiance sans gestionnaire de confiance centralis\u00e9, tandis que les GNN capturent efficacement les relations complexes entre les pairs, conduisant \u00e0 des \u00e9valuations de confiance pr\u00e9cises et robustes. Une avanc\u00e9e importante dans notre protocole est la r\u00e9solution du \u00ab\u00a0probl\u00e8me de d\u00e9marrage \u00e0 froid\u00a0\u00bb ou du \u00ab\u00a0probl\u00e8me du score de confiance initial\u00a0\u00bb. Pour y parvenir, le pair d’amor\u00e7age adopte des marches al\u00e9atoires pour s\u00e9lectionner des pairs fiables parmi ses voisins, garantissant une approche d\u00e9centralis\u00e9e sans d\u00e9pendre d’une entit\u00e9 centralis\u00e9e ou de pairs pr\u00e9d\u00e9finis. Contrairement aux solutions existantes, cette m\u00e9thode \u00e9vite de submerger les pairs les plus dignes de confiance du r\u00e9seau. Un autre d\u00e9fi abord\u00e9 dans les syst\u00e8mes de r\u00e9putation est la r\u00e9ticence des pairs \u00e0 fournir des r\u00e9troactions n\u00e9gatives, souvent par peur de repr\u00e9sailles ou simplement en ne fournissant pas de r\u00e9troaction du tout. Pour r\u00e9soudre ces probl\u00e8mes, nous introduisons un m\u00e9canisme d’incitation qui encourage les r\u00e9troactions sinc\u00e8res et nous mettons en \u0153uvre des m\u00e9canismes sp\u00e9cialis\u00e9s pour sanctionner les comportements mauvais ou paresseux. Ces innovations favorisent un processus d’\u00e9valuation de confiance plus fiable et \u00e9quilibr\u00e9 au sein du syst\u00e8me. De plus, nous proposons une variante de BTrust appel\u00e9e GBTrust, qui am\u00e9liore le protocole original en incorporant des Graph Neural Networks (GNN) et un nouveau m\u00e9canisme bas\u00e9 sur l’attention sp\u00e9cifiquement con\u00e7u pour la gestion de la confiance. Cette variante permet d’am\u00e9liorer la d\u00e9tection des pairs malveillants dynamiques et renforce la robustesse et la pr\u00e9cision globale de l’\u00e9valuation de la confiance. En utilisant les GNN, GBTrust capture efficacement les relations complexes et les comportements dynamiques des pairs dans le r\u00e9seau, permettant ainsi une identification plus pr\u00e9cise des activit\u00e9s malveillantes et une meilleure adaptabilit\u00e9 aux dynamiques de confiance changeantes. Le m\u00e9canisme bas\u00e9 sur l’attention renforce \u00e9galement la capacit\u00e9 du mod\u00e8le \u00e0 prioriser et \u00e0 pond\u00e9rer diff\u00e9rents facteurs de confiance, conduisant \u00e0 des \u00e9valuations de confiance plus fiables et pr\u00e9cises. Nous d\u00e9montrons l’efficacit\u00e9 du syst\u00e8me GBTrust propos\u00e9 dans des r\u00e9seaux P2P \u00e0 grande \u00e9chelle en utilisant des simulations d’un r\u00e9seau P2P, et nous montrons que BTrust est hautement r\u00e9silient aux pannes et robuste contre les n\u0153uds malveillants.<\/p>\n\n\n\n
Abstract :<\/strong><\/p>\n\n\n\nThe emergence of blockchain technology and cryptocurrencies has enabled the development of innovative peer-to-peer (P2P) models for resource allocation, sharing, and monetization. As these P2P models operate without inherent trust, the need for reliable trust and reputation mechanisms becomes crucial to minimize potential risks associated with engaging with malicious peers. Several trust management systems (TMS) have been proposed to establish trust in traditional P2P networks, aiming to facilitate the selection of dependable resources and deter peer misbehavior, with a significant focus on utilizing reputation as a guiding factor. Reputation-based trust systems (RTMS) play a fundamental role by leveraging community-based reputations to establish trust. They enable peers to assess the trustworthiness of others and evaluate the Quality of Service (QoS) based on shared reputations and past interactions. While these systems establish a peer-to-peer overlay trust network, the majority of these protocols are not tailored to suit Blockchain-based networks, resulting in various shortcomings due to their outdated design. This thesis presents our protocol BTrust, a novel decentralized and modular trust management system for large-scale P2P networks, leveraging blockchain technology and (Graph Neural Network) GNN for trust evaluation. BTrust introduces a multi-dimensional trust and reputation model to assess peer trustworthiness, dynamically deriving a single value from multiple parameters. The blockchain ensures reliable trust computation, dissemination, and storage without a central trust manager. An important breakthrough in our protocol is the resolution of the \u00ab\u00a0cold start\u00a0\u00bb or \u00ab\u00a0initial trust score problem\u00a0\u00bb. To achieve this, the bootstrapping peer adopts random walks to select trustworthy peers among its neighbors, ensuring a decentralized approach without relying on any centralized entity or predefined peers. Unlike existing solutions, this method prevents overwhelming the most trusted peers in the network. Another challenge addressed in reputation systems is the reluctance of peers to provide negative feedback, often due to fear of retaliation or simply not providing feedback at all. To tackle these issues, we introduce an incentive mechanism that encourages truthful feedback and implement specialized mechanisms to penalize bad or lazy behavior. These innovations promote a more reliable and balanced trust evaluation process within the system. Furthermore, we propose a variant of BTrust called GBTrust, which improves upon the original protocol by incorporating Graph Neural Networks (GNNs) and a novel attention-based mechanism specifically designed for trust management. This variant enhances the detection of dynamic malicious peers and strengthens the overall robustness and accuracy of trust evaluation. By leveraging GNNs, GBTrust effectively captures the complex relationships and dynamic behavior of peers in the network, enabling more accurate identification of malicious activities and better adaptability to changing trust dynamics. The attention-based mechanism further enhances the model’s ability to prioritize and weigh different trust factors, leading to more reliable and precise trust assessments. We demonstrate the efficiency of the proposed protocol in large-scale P2P networks using simulations of a P2P network and show that BTrust and its variant (GBTrust) are highly resilient to failures and robust against malicious nodes.<\/p>\n","protected":false},"excerpt":{"rendered":"
L’Ecole doctorale : Ecole Doctorale de l’Institut Polytechnique de Paris et le Laboratoire de recherche SAMOVAR – Services r\u00e9partis, Architectures, Mod\u00e9lisation, Validation, Administration des R\u00e9seaux pr\u00e9sentent l\u2019AVIS DE SOUTENANCE de Monsieur Badr BELLAJ Autoris\u00e9 \u00e0 pr\u00e9senter ses travaux en vue de l\u2019obtention du Doctorat de l’Institut Polytechnique de Paris, pr\u00e9par\u00e9 \u00e0 T\u00e9l\u00e9com SudParis en : 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