Developing threat detection and weather impact techniques by AI algorithms to enhance the reliability of FSO/RF system

Abstract

Free space optical (FSO) and radio frequency (RF) communication systems need artificial intelligence (AI) to increase their reliability against cyber threats, as well as the vagaries of bad weather. This paper presents a new AI-decision layer of operation of a hybrid FSO/RF system what dynamically ensures its security and operational stability in case of environmental (fog/dust) and security (eavesdropping/jamming) threats. The authors’ technique fundamentally juxtaposes fuzzy logic rule-based classification with multi-algorithm machine learning (ML) validation (54 actionable rules k-nearest neighbours (KNN), support vector machine (SVM), artificial neural networks (ANN)) towards 99.9% real-time response optimization, vastly superior to conventional threshold-based applications. To the authors’ knowledge, this is the first architecture to accommodate adaptive channel switching/encryption in the < 0.1 ms latency regime while maintaining the high-speed benefits of FSO. Experimental results show that in terms of accuracy, error rate, and the balance between precision and recall, ANN is superior to KNN and SVM. ANN achieves the highest classification accuracy with the fewest false positive rates. The significance of the results lies in their ability to improve the security and efficiency of hybrid FSO/RF systems in a way that requires minimal human intervention.