Channel characteristics estimation based on a secure optical transmission system with deep neural networks

Abstract

Optical transmission security has attracted much attention. In recent years, many secure optical transmission systems based on channel characteristics are proposed. However, there are many drawbacks with these systems, such as separated plaintext and key transmission, low key generation rate (KGR), insecurity when the eavesdropper has acquired the lengths of the local fibers utilized by legal parties. To solve the above problems, we propose a novel secure optical transmission system based on neural networks (NNs), which are employed to estimate channel characteristics. By training NNs locally and transmitting pseudo-keys, the proposed system can transmit the plaintext together with key, transforming the key dynamically. Moreover, since the channel characteristics for legal parties and eavesdropper are not completely identical, the NNs trained by legal parties and eavesdropper are inconsistent. Even though the eavesdropper has attained the lengths of local fibers wielded by legal parties, the NN model trained by the legal parties is still unavailable to illegal eavesdropper. The final key is generated by the trained NN and pseudo-key, so the keys generated by legal parties and eavesdropper are dissimilar. The simulation results prove the feasibility of the proposed system with the transmission distance of 100 km and the bit rate of 100 Gbps. Meanwhile, if plaintext and key have equivalent code length, the KGR of 50 Gbps for legal parties and the key disagreement rate (KDR) of 50% for illegal eavesdropper will be realized.