Fiber Physical Coding Recognition and Secure Communication Based on Machine Learning

The quality of fiber optic links has a direct impact on the performance of power communication systems, and their stability and reliability are key factors for the efficient operation of modern communication networks. In fiber optic communication systems, fiber optic physical coding recognition plays a crucial role. This process requires precise decoding and efficient identification of complex signals transmitted through optical fibers to ensure data integrity and accuracy. However, with the continuous advancement of fiber optic communication technology and the expansion of its application fields, its security issues have become increasingly prominent, becoming a key factor restricting its further development. In response to this challenge, this article proposes a machine learning (ML) based optical fiber physical coding recognition and secure communication algorithm. This algorithm fully utilizes the powerful ability of ML in processing complex data pattern recognition and classification tasks, combined with the characteristics of fiber optic communication systems, to achieve accurate recognition of fiber optic physical encoding, while enhancing the security of the communication process. The experimental results show that the algorithm exhibits excellent performance in fiber physical coding recognition, not only with high recognition accuracy, but also with good robustness and generalization ability.