Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
4-1-2021 12:00 AM
End Date
9-1-2021 12:00 AM
Description
Strong authentication is crucial as wireless networks become more widespread and relied upon. The robust physical layer features produced by advanced communication networks lend themselves to accomplishing physical layer authentication by using channel state information (CSI). The use of deep learning with neural networks is well suited for classification tasks and can further the goal of enhancing physical layer security. To that end, we propose a semi-supervised generative adversarial network to differentiate between legitimate and malicious transmitters and accurately identify devices for authentication across a range of signal to noise ratio conditions. Our system leverages multiple input multiple output CSI across orthogonal frequency division multiplexing subcarriers using a small percentage of labeled training data.
Multi-subcarrier Physical Layer Authentication Using Channel State Information and Deep Learning
Online
Strong authentication is crucial as wireless networks become more widespread and relied upon. The robust physical layer features produced by advanced communication networks lend themselves to accomplishing physical layer authentication by using channel state information (CSI). The use of deep learning with neural networks is well suited for classification tasks and can further the goal of enhancing physical layer security. To that end, we propose a semi-supervised generative adversarial network to differentiate between legitimate and malicious transmitters and accurately identify devices for authentication across a range of signal to noise ratio conditions. Our system leverages multiple input multiple output CSI across orthogonal frequency division multiplexing subcarriers using a small percentage of labeled training data.
https://aisel.aisnet.org/hicss-54/st/cyber_systems/3