Machine learning methods for anomaly detection in the radio frequency spectrum: research methodology

Abstract

The experience of the past three years of full-scale warfare testifies to the dynamic transformation of the conceptual foundations of combat operations and the shifting prioritization of the means employed to conduct them. The emergence and increasingly active use of various unmanned systems, the widespread deployment of precision-guided munitions, and the development of advanced electronic warfare capabilities have collectively underscored the strategic significance of the radio frequency spectrum. The provision of continuous spectral monitoring and the detection of anomalous activity in the electromagnetic environment have become critically important components of electronic warfare systems, signals intelligence, and secure communications networks. Traditional approaches to signal analysis – based on fixed thresholds, heuristic rules, or a priori statistical assumptions – are proving insufficiently effective in the highly dynamic and noiseintensive environment of the modern electromagnetic battlespace. In this context, there arises a need to investigate innovative approaches, particularly machine learning methods, for their ability to enable the automatic detection of anomalous signals without reliance on labeled data. Such solutions are expected to enhance the accuracy, adaptability, and response speed of spectral monitoring systems. A research methodology is proposed to assess the feasibility of applying machine learning methods to the task of anomaly detection in the radio frequency spectrum, taking into account the complexity of the data structure, its high dimensionality, and the limited availability of a priori information regarding anomalous samples. This research methodology encompasses the following stages: formulation of the experimental task; selection of anomaly detection methods for experimental evaluation; determination of evaluation metrics; selection and/or generation of test datasets; direct execution of the experimental study; analysis and assessment of the results; visualization and interpretation of the obtained findings; formulation of conclusions based on the experimental outcomes. This article focuses on the theoretical framework of the experimental study. Practical results will be published separately.