| Full text | |
| Author(s): |
Bezerra, Govinda M. G.
;
Ferreira, Tadeu
;
Mattos, Diogo M. F.
;
Zhani, MF
;
Limam, N
;
Borylo, P
;
Boubendir, A
;
DosSantos, CRP
Total Authors: 8
|
| Document type: | Journal article |
| Source: | 25TH CONFERENCE ON INNOVATION IN CLOUDS, INTERNET AND NETWORKS (ICIN 2022); v. N/A, p. 8-pg., 2022-01-01. |
| Abstract | |
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) | |
| FAPESP's process: | 18/23062-5 - MEGACHAIN: blockchain for integration, privacy and audit of megacity systems |
| Grantee: | Célio Vinicius Neves de Albuquerque |
| Support Opportunities: | Regular Research Grants |