Spectrum splitter user detection

Addressing these challenges, this paper introduces a Machine Learning (ML) based architecture for network operators to detect and attribute interferences to specific OSaaS users while blind to the users' internal spectrum details. With the growing demand for high-bandwidth, low-latency applications, Optical Spectrum as a Ser-vice (OSaaS) is of interest for flexible bandwidth allocation within Elastic Optical Networks (EONs) and Open Line Systems (OLS). While OSaaS facilitates transparent connectivity and resource sharing. A spectrum splitter is an optical device designed to separate light or other forms of electromagnetic energy into its component wavelengths. This process is fundamentally different from a simple power divider, which merely reduces signal strength across multiple outputs. This creates two separate paths, which later overlap and interfere. This interference holds information about the light's wavelengths. The detector then turns this into usable data. The spectrum sensing component utilized an. For the first solution, a new stacking model-based malicious users detection is proposed, using two innovative techniques, including chaotic compressive sensing technique-based authentication for feature extraction with a minimum of measurements and an ensemble machine learning technique for users.