Due to the heterogeneity, fragmentation, and lack of visibility, Internet of Things has become the new target for attacks. Therefore, it is necessary for Internet Service Providers to identify IoT devices to prevent attacks and protect the entire network in time. In this paper, we propose an IoT device identification approach based on lightweight deep learning models using a single feature. Specifically, we analyze the traffic pattern specific to IoT devices and use one feature to characterize this pattern, reducing the time consumption. Moreover, we select multiple time scales to extract this feature for different IoT devices, achieving an accurate characterization and improving the accuracy. Furthermore, we use unidirectional flows as analysis objects, suitable for backbone networks. The evaluation results on real-world datasets show that our approach achieves an accuracy of over 99%, with one-seventeenth of the time consumption of the state-of-the-art approach, realizing the lightweight and real-time requirements.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.