Class-Incremental Learning for Wireless Device Identification in IoT

05/08/2021
by   Yongxin Liu, et al.
8

Deep Learning (DL) has been utilized pervasively in the Internet of Things (IoT). One typical application of DL in IoT is device identification from wireless signals, namely Non-cryptographic Device Identification (NDI). However, learning components in NDI systems have to evolve to adapt to operational variations, such a paradigm is termed as Incremental Learning (IL). Various IL algorithms have been proposed and many of them require dedicated space to store the increasing amount of historical data, and therefore, they are not suitable for IoT or mobile applications. However, conventional IL schemes can not provide satisfying performance when historical data are not available. In this paper, we address the IL problem in NDI from a new perspective, firstly, we provide a new metric to measure the degree of topological maturity of DNN models from the degree of conflict of class-specific fingerprints. We discover that an important cause for performance degradation in IL enabled NDI is owing to the conflict of devices' fingerprints. Second, we also show that the conventional IL schemes can lead to low topological maturity of DNN models in NDI systems. Thirdly, we propose a new Channel Separation Enabled Incremental Learning (CSIL) scheme without using historical data, in which our strategy can automatically separate devices' fingerprints in different learning stages and avoid potential conflict. Finally, We evaluated the effectiveness of the proposed framework using real data from ADS-B (Automatic Dependent Surveillance-Broadcast), an application of IoT in aviation. The proposed framework has the potential to be applied to accurate identification of IoT devices in a variety of IoT applications and services. Data and code available at IEEE Dataport (DOI: 10.21227/1bxc-ke87) and <https://github.com/pcwhy/CSIL>

READ FULL TEXT

page 1

page 5

page 6

page 9

research
01/25/2021

Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey

The Internet of Things (IoT) is becoming an indispensable part of everyd...
research
11/03/2022

Machine Learning Methods for Device Identification Using Wireless Fingerprinting

Industrial Internet of Things (IoT) systems increasingly rely on wireles...
research
04/08/2021

Zero-bias Deep Learning Enabled Quick and Reliable Abnormality Detection in IoT

Abnormality detection is essential to the performance of safety-critical...
research
11/17/2020

The Case for Retraining of ML Models for IoT Device Identification at the Edge

Internet-of-Things (IoT) devices are known to be the source of many secu...
research
12/11/2021

Automated Customization of On-Thing Inference for Quality-of-Experience Enhancement

The rapid uptake of intelligent applications is pushing deep learning (D...
research
10/12/2021

Zero-bias Deep Neural Network for Quickest RF Signal Surveillance

The Internet of Things (IoT) is reshaping modern society by allowing a d...
research
07/20/2022

Towards Transmission-Friendly and Robust CNN Models over Cloud and Device

Deploying deep convolutional neural network (CNN) models on ubiquitous I...

Please sign up or login with your details

Forgot password? Click here to reset