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Tracing Your Smart-Home Devices Conversations: A Real World IoT Traffic Data-Set.
Anagnostopoulos, Marios; Spathoulas, Georgios; Viaño, Brais; Augusto-Gonzalez, Javier.
Afiliação
  • Anagnostopoulos M; Department of Information Security and Communication Technology, Norwegian University of Science and Technology, 2815 Gjøvik, Norway.
  • Spathoulas G; Department of Information Security and Communication Technology, Norwegian University of Science and Technology, 2815 Gjøvik, Norway.
  • Viaño B; TELEVES S.A.U., 15706 Santiago de Compostela, Spain.
  • Augusto-Gonzalez J; Agata Technology, 15172 A Coruña, Spain.
Sensors (Basel) ; 20(22)2020 Nov 18.
Article em En | MEDLINE | ID: mdl-33218082
Smart-home installations exponential growth has raised major security concerns. To this direction, the GHOST project, a European Union Horizon 2020 Research and Innovation funded project, aims to develop a reference architecture for securing smart-homes IoT ecosystem. It is required to have automated and user friendly security mechanisms embedded into smart-home environments, to protect the users' digital well being. GHOST project aims to fulfill this requirement and one of its main functionalities is the traffic monitoring for all IoT related network protocols. In this paper, the traffic capturing and monitoring mechanism of the GHOST system, called NDFA, is presented, as the first mechanism that is able to monitor smart-home activity in a holistic way. With the help of the NDFA, we compile the GHOST-IoT-data-set, an IoT network traffic data-set, captured in a real world smart-home installation. This data-set contains traffic from multiple network interfaces with both normal real life activity and simulated abnormal functioning of the devices. The GHOST-IoT-data-set is offered to the research community as a proof of concept to demonstrate the ability of the NDFA module to process the raw network traffic from a real world smart-home installation with multiple network interfaces and IoT devices.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Sensors (Basel) Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Noruega

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Sensors (Basel) Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Noruega