Airports, as Critical National Infrastructure (CNI), operate as tightly coupled socio-technical systems exposed to multifaceted threats, including cyber, physical, social, environmental, and Chemical, Biological and Radiological (CBR) threats. This study presents a structured review of the synthesis of conceptual frameworks, airport structural configurations, sensor networks, and multi-domain threat landscapes, as well as airport security and resilience analysis, while comparatively examining risk assessment approaches. The review shows that existing approaches are effective for threat identification and prioritisation but remain predominantly static, with limitations in scalability, data dependency, and real-time applicability. To address these limitations, Threat-Vulnerability-Risk Assessment (TVRA) is adopted as a structured, reusable approach to support metric allocation, redundancy design, and emergency capability development. It further serves as a bridge between traditional risk assessment and resilience-oriented system design by enabling the transformation of static risk scores into scenario-based inputs, thereby supporting stress-testing and lifecycle-based resilience planning across the prepare, act, and recover phases. However, its inherently static structure limits its ability to capture temporal dynamics and cascading interdependencies, highlighting the need to integrate it with dynamic modelling approaches.

This paper introduces an adaptive hierarchical control framework with multimodal sensing for reliable multirotor unmanned aerial vehicle (UAV) landings in unstructured environments. A bio-inspired rocker-based landing mechanism is designed to minimize control coupling and provide terrain adaptation redundancy. We develop a Lagrangian dynamic model with real-time attitude perception and an adaptive sliding mode control (ASMC) strategy, supported by Lyapunov stability analysis for estimating the disturbance and a bounded tracking error. For terrain perception, a light detection and ranging (LiDAR)-based dynamic terrain flatness scanning and fusion (DTFSF) algorithm achieves over 96% recognition accuracy on basic terrains with a 1-ms processing time across various scenarios. Simulations show that the ASMC reduces the root mean square error by 77.0% compared with proportional–integral–derivative (PID) control, with a 2.2% increase in energy consumption, while maintaining robustness to faults in the actuator and sensor. Field tests confirm its real-time operation at 1 kHz on Cortex-M7 embedded platforms, with power consumption below 20 W.