Abstract
The paper deals with problematic issues of information security in cyber-physical systems. Performance analysis of autonomous objects has been carried out. An information security monitoring system model based on the characteristics resulting from the analysis of electromagnetic radiation from electronic components in standalone devices of cyber-physical systems is presented. A typical scheme for determining the state of a system is shown. Due to the features of equipment sustaining the infrastructure, assessment of an information security state is aimed at analyzing normal system operation rather than searching for signatures and characteristics of anomalies during various types of information attacks. An experiment that provides statistical information on the operation of remote devices of cyber-physical systems has been disclosed, whereby data for decision-making are accumulated by comparing statistical information. The experimental results on information influence on a typical system are presented. The proposed approach for analyzing statistical data of standalone devices based on a naive Bayesian classifier can be used to determine information security states. A special feature of the approach is the ability to rapid adaptation and application of various mathematical tools and machine learning methods to achieve a desired quality of probabilistic evaluation.
Implementation of this type of monitoring does not require a development of complex system applications while allowing implementation of various architectures for system construction that are capable of processing on-board an autonomous object or of communicating data and calculating the state on external computer nodes of monitoring and control systems.
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Semenov, V., Sukhoparov, M., Lebedev, I. (2018). An Approach to Classification of the Information Security State of Elements of Cyber-Physical Systems Using Side Electromagnetic Radiation. In: Galinina, O., Andreev, S., Balandin, S., Koucheryavy, Y. (eds) Internet of Things, Smart Spaces, and Next Generation Networks and Systems. NEW2AN ruSMART 2018 2018. Lecture Notes in Computer Science(), vol 11118. Springer, Cham. https://doi.org/10.1007/978-3-030-01168-0_27
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