| Texto completo | |
| Autor(es): |
Bezerra, Govinda M. G.
;
Ferreira, Tadeu
;
Mattos, Diogo M. F.
;
Zhani, MF
;
Limam, N
;
Borylo, P
;
Boubendir, A
;
DosSantos, CRP
Número total de Autores: 8
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 25TH CONFERENCE ON INNOVATION IN CLOUDS, INTERNET AND NETWORKS (ICIN 2022); v. N/A, p. 8-pg., 2022-01-01. |
| Resumo | |
Devices from the Internet of Things increasingly mediate a significant number of essential everyday activities. IoT devices empower homes, industries, and offices, monitoring, sensing, and acting ubiquitously and stealthily. However, each device produces a network fingerprint that leaks information about users' behaviors and routines. This paper proposes a flow representation method for precise recognition of different types of IoT Devices. Our proposal relies on a tensor representation of the network flows to retrieve spatial and temporal correlation of flows. We show that our proposal achieves up to 99% precision on classifying IoT network flows using machine learning algorithms, such as Convolution Neural Networks, Recurrent Neural Networks and boosted decision trees. (AU) | |
| Processo FAPESP: | 18/23062-5 - MEGACHAIN: blockchain para integração, privacidade e auditoria de sistemas de megacidades |
| Beneficiário: | Célio Vinicius Neves de Albuquerque |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |