Cybersecurity Threats to Networked Unmanned Systems: A Desk Study of Vulnerabilities and Countermeasures

Abstract

The increasing connectivity and autonomy of unmanned systems create expanding attack surfaces for cyber threats that can compromise mission success, endanger human safety, and enable adversarial exploitation of critical capabilities. This comprehensive desk study systematically examines cybersecurity vulnerabilities in networked unmanned systems and analyzes countermeasures spanning prevention, detection, and recovery. We categorize cyber threats by attack vector: communication link exploitation including jamming, spoofing, and man-in-the-middle attacks; sensor spoofing through GPS falsification or adversarial perturbations to camera inputs; software vulnerabilities enabling unauthorized access; supply chain compromises introducing malicious components; and insider threats from malicious personnel. Attack objectives are analyzed spanning denial of service, hijacking for unauthorized control, data exfiltration, and physical damage through malicious commands. Particular emphasis is placed on attacks exploiting autonomy features including adversarial machine learning crafting inputs causing misclassification, poisoning attacks corrupting training data, and logic exploitation inducing unsafe behaviors. Vulnerabilities specific to multi-vehicle systems are examined including consensus protocol attacks, Sybil attacks with fake identities, and Byzantine attacks with compromised agents. Countermeasures are comprehensively reviewed organized by defense layer: physical layer security including spread spectrum communications and quantum key distribution; network layer protections including authentication protocols and intrusion detection; application layer defenses including secure coding and runtime monitoring; and architecture approaches including defense-in-depth and least privilege access. Cryptographic approaches are analyzed balancing security with computational constraints. Anomaly detection techniques are examined including statistical methods, specification-based approaches, and machine learning classifiers. Resilient control architectures maintaining safe operation despite attacks are discussed including attack-resilient state estimation and secure control protocols. The study critically assesses unique challenges including resource constraints, real-time requirements, and physical accessibility. Case studies from documented incidents are synthesized including GPS spoofing and ransomware attacks.