Deep learning for eavesdropper detection in free-space optical ON-OFF keying
Abstract
We demonstrate the efficacy of machine learning techniques in the detection of an eavesdropper in a free-space optical (FSO) communications setup. Experimentally, we use ON-OFF keying (OOK) and send strings of random bits through strong turbulence. When we apply a simulated eavesdropper to the bits in the post processing stage, a deep learning convolutional neural network (CNN) is able to successfully detect whether or not the eavesdropper is present. We vary the strength and duration of the attenuation of the simulated eavesdropper, and vary the signal-to-noise ratio (SNR) of the bit streams, and find that the strength of the eavesdropper has the greatest effect on eavesdropper detection accuracy. We are hopeful this flexible approach may be used in current and future operational FSO communications systems.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 18, 2022
- Source ID
- 10.1364/optcon.451308
Entities
People
- Nicholas J. Savino
- Ryan T. Glasser
- Sanjaya Lohani
Organizations
- Office of Naval Research
- Tulane University of Louisiana
- University of Illinois Urbana–Champaign