مؤتمر
Federated Learning as enabler for Collaborative Security between not Fully-Trusting Distributed Parties
| العنوان: | Federated Learning as enabler for Collaborative Security between not Fully-Trusting Distributed Parties |
|---|---|
| المؤلفون: | Lavaur, Léo, Costé, Benjamin, Pahl, Marc-Oliver, Busnel, Yann, Autrel, Fabien |
| المساهمون: | Département Systèmes Réseaux, Cybersécurité et Droit du numérique (IMT Atlantique - SRCD), IMT Atlantique (IMT Atlantique), Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT), Objets communicants pour l'Internet du futur (OCIF), Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT)-RÉSEAUX, TÉLÉCOMMUNICATION ET SERVICES (IRISA-D2), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Airbus CyberSecurity Rennes, Airbus CyberSecurity SAS Élancourt, mEasuRing and ManagIng Network operation and Economic (ERMINE), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-RÉSEAUX, TÉLÉCOMMUNICATION ET SERVICES (IRISA-D2), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), This research is part of the chair CyberCNI.fr with support of the FEDER development fund of the Brittany region. |
| المصدر: | CEUR Workshop Proceedings (CEUR-WS.org) ; C&ESAR 2022 - 29th Computer & Electronics Security Application Rendezvous ; https://imt-atlantique.hal.science/hal-03831515Test ; C&ESAR 2022 - 29th Computer & Electronics Security Application Rendezvous, Nov 2022, Rennes, France. pp.1-16 |
| بيانات النشر: | HAL CCSD |
| سنة النشر: | 2022 |
| المجموعة: | Université de Rennes 1: Publications scientifiques (HAL) |
| مصطلحات موضوعية: | Federated learning, cybersecurity, intrusion detection, distributed trust, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], [INFO.INFO-IT]Computer Science [cs]/Information Theory [cs.IT], [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR] |
| جغرافية الموضوع: | Rennes, France |
| الوصف: | International audience ; Literature shows that trust typically relies on knowledge about the communication partner. Federated learning is an approach for collaboratively improving machine learning models. It allows collaborators to share Machine Learning models without revealing secrets, as only the abstract models and not the data used for their creation is shared. Federated learning thereby provides a mechanism to create trust without revealing secrets, such as specificities of local industrial systems. A fundamental challenge, however, is determining how much trust is justified for each contributor to collaboratively optimize the joint models. By assigning equal trust to each contribution, divergence of a model from its optimum can easily happen-caused by errors, bad observations, or cyberattacks. Trust also depends on how much an aggregated model contributes to the objectives of a party. For example, a model trained for an OT system is typically useless for monitoring IT systems. This paper shows first directions how heterogeneous distributed data sources could be integrated using federated learning methods. With an extended abstract, it shows current research directions and open issues from a cyber-analyst's perspective. |
| نوع الوثيقة: | conference object |
| اللغة: | English |
| العلاقة: | hal-03831515; https://imt-atlantique.hal.science/hal-03831515Test; https://imt-atlantique.hal.science/hal-03831515/documentTest; https://imt-atlantique.hal.science/hal-03831515/file/CESAR_2022.pdfTest |
| الإتاحة: | https://imt-atlantique.hal.science/hal-03831515Test https://imt-atlantique.hal.science/hal-03831515/documentTest https://imt-atlantique.hal.science/hal-03831515/file/CESAR_2022.pdfTest |
| حقوق: | info:eu-repo/semantics/OpenAccess |
| رقم الانضمام: | edsbas.21E373A5 |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |