# Advanced Control Strategies for Securing UAV Systems: A Cyber-Physical Approach
###### Abstract
[9] focuses on a comprehensive approach to threat modeling and security assessments, yet it lacks extensive real-world validation. Ref. [10] offers an in-depth classification of vulnerabilities and countermeasures but is limited by a theoretical scope, providing little insight into practical implementations. Ref. [11] emphasizes the integration of advanced navigation algorithms and AI for UAV surveillance, though it falls short in addressing real-world adaptability. Similarly, Ref. [15] delivers a robust taxonomy of cybersecurity threats and defense mechanisms but does not thoroughly test these in dynamic environments. Additionally, Refs. [12; 13] explore emerging defense technologies and AI-based security enhancements, yet they too are constrained by limited empirical validation. Collectively, these surveys underscore the urgent need for further empirical research and validation to ensure the practical effectiveness of proposed UAV security strategies across diverse operational scenarios. Nonetheless, Ref. [16] highlights the latest advancements in UAV technology, focusing on enhanced detection mechanisms like radio frequency and radar-based systems alongside robust security measures such as jamming and signal redirection to protect against high-assault threats. In a similar vein, Ref. [17] reviews the security challenges facing drones, emphasizing vulnerabilities like unencrypted communication channels that expose UAVs to hacking and control takeovers. Both papers stress the need for improved detection, encryption, secure communication protocols, and advanced anomaly-detection systems to defend against cyber and physical threats. Additionally, Refs. [18; 19] outline the increasing complexity of these threats, advocating for the integration of emerging technologies like mmWave, massive MIMO, cognitive radio, machine learning, and the blockchain to enhance UAV security. The reviews collectively underscore the critical importance of comprehensive cybersecurity frameworks in maintaining the integrity and safety of UAV operations. Given the dual threats faced by UAV systems, including physical attacks like interception and sabotage, as well as cyber-threats such as hacking and data breaches, a combined cyber-physical security approach is essential. Advanced non-linear control strategies, like sliding mode control, adaptive control, and robust control, are critical in ensuring responsive and adaptable UAV operations. In line with this, [PERSON]. [20] presents an approach to enhancing UAV safety by detecting and compensating for attacks on flight trajectories, particularly when trajectory data are compromised. The study introduces attack detection and compensation mechanisms, including an attack observer (AO) and learning observer (LO), which effectively distinguish between genuine disturbances and malicious attacks, as demonstrated through simulations and experimental tests. Focusing on sliding mode control (SMC), its inherent robustness against disturbances and its ability to maintain performance under uncertain conditions make it an ideal choice for dealing with unpredictable security challenges in UAV operations. To address these challenges, the author's previous works have concentrated on developing robust and adaptive control methods for multi-ducted fan platforms aimed at maintaining system stability during payload release and firefighting applications. Specifically, model reference adaptive control (MRAC) was employed to counteract internal uncertainties, such as changes in weight, while sliding mode control (SMC) algorithms were designed to mitigate the effects of external disturbances [21; 22; 23; 24]. Nevertheless, Ref. [25] addresses the security and formation tracking of multi-UAV systems under replay attacks by introducing an observer-based event-triggered sliding mode control approach, which optimizes communication-based on attack severity to reduce data transmission and energy consumption while maintaining performance. Similarly, Ref.