Physical Layer Dynamic Key Encryption in OFDM-PON System Based on Cellular Neural Network

In this paper, we propose a dynamic key technique based on Cellular Neural Network (CNN) for security improvement in the orthogonal frequency division multiplexing passive optical network (OFDM-PON). To enhance the encryption scheme security, a six-dimensional CNN hyperchaotic system is employed to encrypt the data. And, the keys are divided into the dynamic and static. The dynamic key is randomly extracted from a key set by incorporating the random feature of the input data. Then, the chaotic sequence generated by the dynamic key is served as the synchronous sequence for encryption. Moreover, the chaotic sequences generated by the static keys are used to resist the chosen-plaintext attacks (CPAs) and scramble the phase of QAM symbols on the frequency domain. With these processing techniques, the multi-fold data encryption can create a key space of ~10
³¹⁵
to protect against the exhaustive trial. The transmission of 10-Gb/s encrypted 16-QAM-based OFDM signal is demonstrated over 20-km single mode fiber (SMF) by experiment. The results show that our proposed scheme can provide excellent confidentiality of data transmission against the CPAs and brute-force attack.