Speaker
Description
Critical infrastructure has become increasingly vulnerable to complex cyber threats characteristic of the contemporary hybrid security environment. The convergence of digital attacks, information operations, and potential physical disruptions necessitates integrated risk assessment approaches that capture both technological and operational dimensions of infrastructure systems. This study presents an integrated risk assessment model for cyber threats targeting critical infrastructure, developed through the combination of artificial intelligence (AI) techniques and expert transport system analysis, with a specific focus on railway infrastructure as a key component of national security.
The proposed model consists of three complementary components: (1) an AI based module for anomaly detection and vulnerability identification within information and control systems; (2) probabilistic modelling of cyberattack and hybrid threat scenarios, enabling the estimation of likelihood and impact across different threat vectors; and (3) a transport logistics assessment of operational resilience, analysing the effects of potential disruptions on railway system performance, capacity, safety, and service continuity.
Simulation results demonstrate that the integrated approach provides higher accuracy in risk prediction, earlier detection of critical deviations, and more effective planning of protective measures. The model enables the identification of the most vulnerable infrastructure elements and supports the development of adaptive response strategies. The study contributes to the advancement of cybersecurity methodologies in the transport sector and offers practical recommendations for enhancing the resilience of critical infrastructure under hybrid threat conditions.