| Autor: |
Fakhruldeen, Hassan Falah, Saadh, Mohamed J., Khan, Samiullah, Salim, Nur Agus, Jhamat, Naveed, Mustafa, Ghulam |
| Zdroj: |
International Journal of Data Science and Analytics; 20240101, Issue: Preprints p1-13, 13p |
| Abstrakt: |
The recognition of smart home devices within WiFi environments stands as a pivotal focus within contemporary Internet of Things (IoT) security, especially in the context of Futuristic Smart Networks-based IoT. The inherent encryption feature of the 802.11 protocol in WiFi settings renders conventional identification methods, reliant on plaintext traffic patterns, ineffective for IoT devices. Through an examination of the 802.11 protocol, distinctive traits within data frames of various smart home devices are revealed. Building on these insights, this research selects attributes like frame length, frame arrival time, duration, and frame sequence number as salient traffic characteristics. Leveraging an enhanced decision tree CART algorithm, the study achieves robust device identification for smart home devices operating within WiFi environments. Experimental outcomes affirm the method’s efficacy by accurately discerning device models, achieving an impressive identification accuracy of 91.3%. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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