Abstract
The identification of the cybersecurity (CS) state of Internet of things (IoT) devices determines the necessity to search for and improve approaches to detecting various threat types. The unification used in the mass development of IoT devices facilitates software and hardware modification to block certain built-in protective functions from the side of a potential intruder. A need arises to develop universal methods for identifying the cybersecurity state of devices using comprehensive approaches to analyzing data from internal and external information channels. The article presents an approach to identifying the cybersecurity of IoT devices based on processing time series recorded from sensors during various processes, and internal and external (thirdparty) sources. The approach is based on classification methods. The presented solution uses template sequences containing synchronized time series showing numerical values obtained from various probes and sensors during process execution. The proposed approach makes it possible to identify IoT device cybersecurity states without increasing the volume of information stored and processed in internal resources.
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