Detecting and responding to attacks and weather effects in hybrid FSO/RF systems using the Dempster-Schaffer theory with AI algorithms
Abstract
In this paper, a new intelligent switch for hybrid Free-Space Optical (FSO) RF communication is proposed for improved reliability and security in the presence of dynamic environmental changes and cyber-attack interferences. Using Dempster-Shafer Theory (DST) for reliable threat classification and ANN, KNN, and SVM for machine learning, an extraordinary real-time communication link selection is achieved. A broad training dataset (10,000 simulated samples), covering eavesdropping and jamming threats, fog and dust effects, was used to train and validate the network. Our work combines DST to combine evidence from multiple sources and make an accurate belief assignment for communication modes. In addition, the system exhibits a high claimed confidence, RF and FSO link beliefs around 0.88-0.89 and 0.82-0.83, respectively. The machine learning models have excellent performance on threat detection and mode classification. ANN, KNN, and SVM obtained accuracies of 0.99986, 0.99984, and 0.99930, respectively. All models achieved near-perfect AUC values, where most classes reach 1, meaning a better discriminative performance. Importantly, the performance of ANN was significantly outperformed by KNN and SVM in all metrics, demonstrating its robustness. This work provides an efficient and dynamic approach to keep the communication in difficult FSO/RF links secure and reliable, and brightens the path for future communication systems.
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