IoT-Portrait: Automatically Identifying IoT Devices via Transformer with Incremental Learning

Juan Wang (), Jing Zhong and Jiangqi Li
Additional contact information
Juan Wang: School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China
Jing Zhong: School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China
Jiangqi Li: School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China

Future Internet, 2023, vol. 15, issue 3, 1-18

Abstract: With the development of IoT, IoT devices have proliferated. With the increasing demands of network management and security evaluation, automatic identification of IoT devices becomes necessary. However, existing works require a lot of manual effort and face the challenge of catastrophic forgetting. In this paper, we propose IoT-Portrait, an automatic IoT device identification framework based on a transformer network. IoT-Portrait automatically acquires information about IoT devices as labels and learns the traffic behavior characteristics of devices through a transformer neural network. Furthermore, for privacy protection and overhead reasons, it is not easy to save all past samples to retrain the classification model when new devices join the network. Therefore, we use a class incremental learning method to train the new model to preserve old classes’ features while learning new devices’ features. We implement a prototype of IoT-Portrait based on our lab environment and open-source database. Experimental results show that IoT-Portrait achieves a high identification rate of up to 99% and is well resistant to catastrophic forgetting with a negligible added cost both in memory and time. It indicates that IoT-Portrait can classify IoT devices effectively and continuously.

Keywords: class incremental learning; deep learning; device fingerprint; Internet of Things; traffic identification (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/15/3/102/pdf (application/pdf)
https://www.mdpi.com/1999-5903/15/3/102/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:15:y:2023:i:3:p:102-:d:1089738

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().