Chaotic physical security scheme based on variational auto-encoder for OFDM-PON

This paper proposed a novel chaotic physical security scheme based on Variational Auto-Encoder (VAE) for optical frequency division multiplexing-passive optical networks (OFDM-PON). We adopt the deep generative model VAE to generate chaotic sequences for the encryption of OFDM symbols. Different chaotic security schemes are included to improve the key space and sensitivity of chaotic models, thus enhancing the security of the OFDM-PON system. With the training materials of different chaotic security schemes, VAE can learn the complex structure of data distribution in various chaotic models and finally has the ability to generate the key group with a large space. Meanwhile, the benchmark performance of the OFDM system is experimentally investigated in terms of the bit error rate (BER). Moreover, owing to the parallel computing of GPU, the time consumed for training of VAE can be reduced to a large extent, and the time for generation of chaotic sequences via VAE is only 1.38% of that via repeated iteration of equations, which highlights the remarkable reduction in complexity of the chaotic physical security scheme.