A High-Resolution Intrusion Detection Framework for Fiber Optic Networks Using Improved OLCR and LSTM-Based Temporal Analysis - Zhang, Bo (Informatica, 2025)
With the increasing demand for data transmission, fiber-optic communication systems face growing challenges in security and real-time monitoring. To address limitations in spatial resolution and weak anomaly detection, this study proposes a high-resolution intrusion detection framework integrating enhanced Optical Low-Coherence Reflectometry (OLCR) and Long Short-Term Memory (LSTM) networks. At the link layer, high-resolution interferometric signal detection and anomaly localization are achieved through spectral shaping, polarization stabilization, and optical path difference modulation. At the system layer, LSTM enables multi-dimensional feature fusion and temporal pattern recognition for intelligent intrusion classification and adaptive defense. Experiments on a 10-km fiber link simulate typical anomalies including breaks, splice faults, and bending eavesdropping, using NSL-KDD and Polarization Mode Dispersion datasets for training and validation. Measured parameters cover reflectivity, phase shift, and polarization angular velocity. Results demonstrate a spatial resolution of 11.15μm at 100 m, detection accuracy of 96.40%, and intrusion recognition rate of 95.60%, outperforming existing methods. The fusion of improved OLCR and LSTM proves effective for high-precision detection and dynamic protection in complex environments, offering a scalable intelligent solution for secure fiber-optic systems.