ИДЕНТИФИКАЦИЯ СОСТОЯНИЯ ОТДЕЛЬНЫХ ЭЛЕМЕНТОВ КИБЕРФИЗИЧЕСКИХ СИСТЕМ НА ОСНОВЕ ВНЕШНИХ ПОВЕДЕНЧЕСКИХ ХАРАКТЕРИСТИК

Прикладная информатика. 2018. № 5(77). С. 72–83.

Семенов В. В., Lebedev I., Сухопаров М. Е.

    
The task of determining information security state of objects using the information of signals of electromagnetic emissions of individual elements of devices of cyber-physical systems was investigated. We consider the main side channels of information with which it is possible to monitor the state of the system and analyze the software and hardware environment. Such «independent» methods of monitoring allow analyzing the state of the system based on external behavioral characteristics within the framework of conceptual models of autonomous agents. The statistical characteristics of signals allowing to identify changes in the state of local devices of systems are considered. Was described an experiment aimed at obtaining statistical information on the operation of individual elements of cyber-physical systems. The efficiency of the neural networks approach for solving the described classification problem, in particular, two-layer feed-forward neural networks with sigmoid hidden neurons was investigated. The results of the experiments showed that the proposed approach is superior to the quality of detection of anomalous states by classification based on internal indicators of the functioning of the system. With minimal time of accumulation of statistical information using the proposed approach based on neural networks, it becomes possible to identify the required state of the system with a probability close to 0.85. The proposed approach of the analysis of the statistical data based on neural networks can be used for definition of states of information safety of independent devices of cyber-physical systems.

Keywords: информационная безопасность, нейронные сети, neural networks, information security