MODEL FOR EVALUATING THE PARAMETERS OF TARGETED INFORMATION ATTACKS ON THE AVIATION TRANSPORT SYSTEM

Abstract

The increasing digitalization of aviation systems has introduced significant vulnerabilities to Advanced Persistent Threats (APTs). Standard intrusion detection systems often fail to identify stealthy, low-intensity information injections due to their dynamic and multi-stage nature. In the context of 2026 aviation security standards, there is a critical need for real-time monitoring tools capable of identifying subtle anomalies in Air Traffic Control (ATC) data flows. Methods: This research proposes a dynamic model of information impact intensity based on the Kalman-Bucy filtering framework and stochastic differential equations. The model utilizes a recursive estimation algorithm to track the state of the aviation information environment. The detection mechanism is centered on the statistical analysis of innovation sequences (residuals) to identify deviations caused by unauthorized cyber-physical influences. Results: The developed model allows for the effective identification of APT attack phase transitions by analyzing the variance of the filter's residuals. Simulation results demonstrate that the application of Kalman filtering significantly improves the detection probability of stealthy threats compared to traditional threshold-based methods. The model accounts for the non-stationary nature of aviation data traffic, providing high sensitivity to small-scale but persistent intensity fluctuations. Conclusions: The integration of the proposed mathematical apparatus into aviation cybersecurity systems enhances the resilience of ATC infrastructure. The findings provide a methodological basis for developing automated decision-support systems for cyber-incident response, ensuring flight safety in the face of evolving global cyber threats.