Deep learning for eavesdropper detection in free-space optical ON-OFF keying

被引:4
作者
Savino, Nicholas J. [1 ]
Lohani, Sanjaya [2 ]
Glasser, Ryan T. [1 ]
机构
[1] Tulane Univ, Dept Phys & Engn Phys, New Orleans, LA 70118 USA
[2] Univ Illinois, Dept Elect & Comp Engn, Chicago, IL 60607 USA
来源
OPTICS CONTINUUM | 2022年 / 1卷 / 12期
关键词
COMMUNICATION; LINK; PERFORMANCE; NETWORKS;
D O I
10.1364/OPTCON.451308
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
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.
引用
收藏
页码:2416 / 2425
页数:10
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