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A Comprehensive Survey on Cybersecurity Challenges and Defenses for Integrated Air, Ground, and Underwater Autonomous Vehicle Systems
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DOI：10.1109/COMST.2026.3708340.png)
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Abstract
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The rapid deployment of autonomous systems, including Connected Autonomous Vehicles (CAVs), Unmanned Aerial Vehicles (UAVs), and Autonomous Underwater Vehicles (AUVs), is enabling coordinated cross-domain operations across transportation, defense, environmental monitoring, and emergency response applications. However, the integration of such heterogeneous platforms introduces significant cybersecurity challenges arising from complex architectures, diverse communication technologies, dynamic operational conditions, and data-driven autonomous decision-making. Existing cybersecurity studies and defense mechanisms often focus on specific autonomous platforms or limited security layers, providing insufficient analysis of integrated cybersecurity interactions across heterogeneous UAV, CAV, and AUV environments. In this survey, we present a comprehensive analysis of cybersecurity threats, vulnerabilities, and defense mechanisms for integrated UAV, CAV, and AUV systems across single-domain, dual-domain, and fully integrated tri-domain operational scenarios. The survey systematically examines security challenges spanning physical, sensing, communication, network, control, and application layers, together with cross-domain attack propagation and collaborative security dependencies. In addition, we review machine learning-based intrusion detection techniques for integrated UAV, CAV, and AUV systems and present HybridEnsemble-ID as an example of a hybrid cybersecurity framework that integrates supervised, unsupervised, semi-supervised, and reinforcement learning approaches for adaptive threat detection in heterogeneous autonomous environments. Furthermore, we consolidate publicly available datasets, simulation platforms, emulators, and hardware-in-the-loop testbeds to support reproducible evaluation and benchmarking of cybersecurity mechanisms for integrated autonomous systems. Finally, we identify open challenges and future research directions, including unified cross-domain evaluation frameworks, deployment-oriented security validation, adaptive defense architectures, and standardized benchmarking methodologies for resilient cross-domain autonomous operations.
Keywords:
Autonomous underwater vehicles
connected autonomous vehicles
Unmanned aerial vehicles
integrated autonomous vehicles
cybersecurity
machine learning
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