Transforming Device Fingerprinting for Wireless Security via Online Multitask Metric Learning

Device fingerprinting is a crucial part in the Internet of Things applications. Existing device-fingerprinting solutions either ignore the influence of the type of network traffic or separately learn a fingerprinting model for each traffic type. This often leads to suboptimal solutions, especially when training data are limited. Considering that the data distributions of different traffic types may be different but related, we propose a novel multitask learning method to learn the fingerprinting models for several traffic types simultaneously. Specifically, we first design a system for device fingerprinting using the popular $k$ -nearest neighbor (KNN) approach. Then, a novel distance metric learning (DML) algorithm termed online multitask metric learning (OMTML) is developed to improve the distance estimation in our system. OMTML enables the models describing different traffic types to help each other during the metric learning procedure, and thus improving their respective accuracies. OMTML can also be updated adaptively, and the updating process is efficient. The experimental results show that the proposed KNN-based system outperforms the artificial neural network (ANN)-based counterpart significantly. Besides, the comparisons of our OMTML and other representative online and multitask DML approaches demonstrate both effectiveness and efficiency of the proposed metric learning method.