Self-Diagnosis as a Way to Increase the Cyber Resistance of Terminal Components of a Technological System

Abstract

The article proposes an approach to determine the technical condition of the terminal components of the technological system, the basis of which are microprocessor systems implemented on software-reconfigurable logic. The existing methods and methods of testing programmable logic integrated circuits are analyzed, the shortcomings and advantages are revealed. It has been proven that the most effective method of using self-diagnosis schemes is BIST — Built-Inself-Test, which in the future can become the basis for monitoring and diagnosing microprocessor systems implemented on a software-reconfigurable element base. The existing methods of determining the technical condition of microprocessor systems implemented on large/very large integrated circuits with rigid architecture are considered, and the mathematical basis of their technical diagnosis is presented. In order to increase the cyber resistance of the terminal components of the technological system, it is proposed to use programmable logic integrated circuits as an element base, which are able to change the internal algorithmic structure by reprogramming as a result of cyber incidents and cyber attacks. At the same time, the reconfiguration of the algorithmic structure of the microprocessor system on the basis of program-reconfigurable logic is proposed to be carried out based on the results of self-diagnosis, that is, by using a diagnostic system with elements of artificial intelligence, which implements the BIST — Built-Inself-Test method. It is assumed that the synergy of the microprocessor system and the diagnostic system with elements of artificial intelligence will allow the implementation of the principle of active fault tolerance (cyber resilience), which consists in the detection and localization of malfunctions (response to cyber incidents and cyber attacks), as well as the restoration of the correct functioning of the terminal components of the technological system by reconfiguring their internal algorithmic structure according to the results of self-diagnosis.