Saint-Petersburg University of the Ministry of Internal Affairs of the Russian Federation (Saint-Petersburg, Russia)

A. A. Nechai, Candidate of Pedagogical Sciences, Associate Professor, webexpromt@mail.ru

The digital transformation of the mining industry is accompanied by the convergence of information and operational technologies, which creates fundamentally new risks of cyberattacks turning into man-made accidents with direct physical consequences. Existing information security assessment methods focus on data protection and do not consider the specifics of mining technological processes. The aim of the work is to develop a methodology for the quantitative assessment of the cyber resilience of mining transport facilities based on mathematical modeling. The methodology includes a two-level network model of the automated process control system topology, considering connections between information and physical nodes, node criticality ranking using the analytic hierarchy process, a probabilistic model of cyberattack propagation based on Markov process theory, regression dependencies of productivity on attack parameters, and expected damage assessment using the Monte Carlo method. The simulation is performed for a typical open-pit mine using autonomous equipment and dispatching systems. It is established that cyberattacks on telemetry and dispatching systems can significantly reduce the efficiency of mining transport equipment, and combined exposure leads to an enhanced negative effect. The scientific novelty lies in the set of interconnected models that make it possible to predict the consequences of cyberattacks for production indicators, as well as in determining the representative volume of the simulation model to obtain statistically reliable results. The practical significance is that the methodology makes it possible to substantiate requirements for automated control system protection at the design stage and integrate cyber risks into the industrial safety system of mining enterprises. Validation of the model using real incident data confirmed the adequacy of the proposed approach.

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