Methodology for Detecting Attacks in the Context of Destructive Influences

Recently, the number of incidents involving unmanned aerial vehicles (UAVs) has increased markedly, which emphasizes the urgent need to improve the level of their security. Given the large number of heterogeneous UAV models on the market, there is an urgent need to unify approaches to data collection and analysis to ensure effective attack detection. A new model for constructing the attack vector of the system is proposed. This model, based on an ontological approach, reflects the relationship between the cyber-physical parameters of the UAV, the attacker's impact, and the consequence. The model allowed identifying unique sets of parameter groups that are associated with specific attacks and other types of destructive impact. In this way, groups of parameters can be identified and the degree to which they are affected by the attack can be assessed. This allows identifying anomalies and preventing possible attacks at an early stage. The developed algorithm is a universal tool that is able to analyze a large amount of heterogeneous data in real time, highlighting anomalous patterns of system behavior and thus significantly reducing the risks of malicious factors. The experimental part demonstrates the effectiveness of the proposed approach in practice, confirming its high performance under various models of UAV operation.