Abstract
In this paper, problematic issues in ensuring the cybersecurity of autonomous unmanned objects were considered. Moreover, prerequisites that determine the need for external monitoring systems were identified. The type and statistical characteristics used for the analysis and classification of sound signals were also shown. The proposed approach to the analysis of the cybersecurity condition of an autonomous object is based on classification methods and allows the identification of the current status based on digitized acoustic information processing. An experiment aimed at obtaining statistical information on various types of unmanned object maneuvers with various arrangements of an audio recorder was conducted. The data obtained was processed using two-layer feed-forward neural networks with sigmoid hidden neurons. Hence, the problem of identifying the cybersecurity condition of autonomous unmanned objects on the basis of processing acoustic signal information obtained through side channels was solved. Digitized information from an acoustic sensor (microphone) located statically in the experiment area was classified more accurately than from the microphone located directly on the autonomous object. With a minimum time of statistical information accumulation using the proposed approach, it becomes possible to identify differences in maneuvers performed by the unmanned object and, consequently, the cybersecurity condition of the object with a probability close to 0.7. The proposed approach for processing signal information can be used as an additional independent element to determine the cybersecurity condition of autonomous objects of unmanned systems. This approach can be quickly adapted using various mathematical tools and machine learning methods to achieve a given quality probabilistic assessment.
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Semenov, V., Sukhoparov, M., Lebedev, I. (2019). Approach to Side Channel-Based Cybersecurity Monitoring for Autonomous Unmanned Objects. In: Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds) Interactive Collaborative Robotics. ICR 2019. Lecture Notes in Computer Science(), vol 11659. Springer, Cham. https://doi.org/10.1007/978-3-030-26118-4_27
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