Adversarial Learning for Hiding Wireless Signals

Provides a novel method for ensuring sensitive messages are secure from malicious intrusion on wireless networks.  Background:
Spectrum access is required to communicate between two mobile nodes in a wireless network, where the communication link can be intercepted or jammed by adversaries due to the inherent broadcast nature of the wireless channel. In this scenario, hiding a secret signal in presence of another mundane ongoing communication is one of the ways to minimize its chances of getting detected or intercepted. Wireless steganography is one method for embedding a secret signal inside another seemingly innocuous signal that acts as a cover to hide the signal of interest. This technology is a wireless steganography technique that hides sensitive content as a form of noise. It uses an adversarial learning model to transform the message into a form that is statistically identical to the hardware noise of a transmitter. A three-node neural network, an encoder, a decoder, and a critic (steganalyzer), are optimized jointly to encode and decode the message while adhering to statistically identical properties of the encoded covert signal and hardware generated noise. Once the learning is complete, this covert signal can be carried by any cover signal, independent of its waveform or modulation order. This covert signal achieves a throughput of 12Mbps at 12dB SNR and is resilient to different levels of hardware noise.  https://suny.technologypublisher.com/files/sites/adobestock_372232239.jpeg Advantages:  
• Independent of waveform of the cover signal.
• Ensures low probability of detection.
• High data rate covert communication.
• Can operate at different levels of target hardware noise.
   Applications:  
The primary application for this technology is ensuring message security on wireless networks. Potential users include:
• Military.
• Homeland Security.
• Government agencies.
• Business.  Intellectual Property Summary:
Patent application filed, U.S. 17/945,508 Stage of Development:
TRL 3 – Experimental proof of concept
Licensing Status:
This technology is available for licensing Licensing Potential:
This technology would be of interest to anyone involved in the manufacture and operation of wireless computer networks. This includes:
• Computer network hardware manufacturers.
• Network software developers.
• Network security professionals.

Website

https://suny.technologypublisher.com/tech/Adversarial_Learning_for_Hiding_Wireless_Signals

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TTO Home Page: https://suny.technologypublisher.com

Name: Thomas Ferguson

Title: Business Development & Licensing Associate

Department: Industry & External Affairs

Email: thomas.ferguson@rfsuny.org

Phone: (518) 434-7067