most citedZero-Bias Deep Learning for Accurate Identification of Internet of Things (IoT) Devices

95 citations · 119 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CR2021

Blockchain Enabled Secure Authentication for Unmanned Aircraft Systems

Yongxin Liu, Jian Wang, Yingjie Chen +5

The integration of air and ground smart vehicles is becoming a new paradigm of future transportation. A decent number of smart unmanned vehicles or UAS will be sharing the national…

cs.LG20212 cited

Zero-bias Deep Neural Network for Quickest RF Signal Surveillance

Yongxin Liu, Yingjie Chen, Jian Wang +3

The Internet of Things (IoT) is reshaping modern society by allowing a decent number of RF devices to connect and share information through RF channels. However, such an open natur…

cs.LG2021

Class-Incremental Learning for Wireless Device Identification in IoT

Yongxin Liu, Jian Wang, Jianqiang Li +2

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-cr…

cs.NI2021

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

Yongxin Liu, Jian Wang, Jianqiang Li +2

Abnormality detection is essential to the performance of safety-critical and latency-constrained systems. However, as systems are becoming increasingly complicated with a large qua…

cs.CR202120 cited

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

Yongxin Liu, Jian Wang, Jianqiang Li +2

The Internet of Things (IoT) is becoming an indispensable part of everyday life, enabling a variety of emerging services and applications. However, the presence of rogue IoT device…

eess.IV20202 cited

Distant Domain Transfer Learning for Medical Imaging

Shuteng Niu, Meryl Liu, Yongxin Liu +2

Medical image processing is one of the most important topics in the field of the Internet of Medical Things (IoMT). Recently, deep learning methods have carried out state-of-the-ar…