RISK IDENTIFICATION OF SECURITY INFORMATION VIOLATIONS IN CYBER-PHYSICAL SYSTEMS BASED ON ANALYSIS OF DIGITAL SIGNALS

Subject of Research. The paper presents an approach to the analysis of digital signal sequences related to cyber-physical systems functioning. The proposed solution combines a set of machine learning methods for analyzing heterogeneous external data of digital signals coming from various system sensors. Method. The methods based on artificial neural networks and the k-nearest neighbors algorithm were studied for the analysis of digital signals. Main Results. The proposed approach has been tested using the signals received from a digital three-axis accelerometer located on an unmanned vehicle prototype. The processing of digital signals by the methods under study has been carried out in the MATLAB R2020a environment. The accuracy of the researched methods has been compared and, as a result, the k-nearest neighbors algorithm reached the value of 96.1 %, whereas artificial neural networks showed the result of 95.0 %. Practical Relevance. The proposed approach makes it possible to detect the risks of information security violations of the cyber-physical systems with acceptable accuracy and can be used in systems for the state monitoring of objects.