Volume 18 • Issue 1 • PP: 34–39 • 2026
Risk-Aware Cyberattack Analytics for Unmanned Aerial Vehicle Communications: A Publication-Ready Gradient- Boosting Framework
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Cyberattack detection in unmanned aerial vehicle environments has become an essential requirement for dependable digital operations. Security analytics for these environments should not only separate benign and malicious traffic, but should also provide interpretable evidence that can support timely triage and intervention. This paper presents a risk-aware classification framework for UAV communication security based on a leakage-screened feature design and a gradient-boosting ensemble model. The framework combines multiclass discrimination, probability-based decision logic, and feature-level interpretation within one coherent workflow. The study demonstrates that a carefully designed ensemble approach can provide balanced and operationally meaningful cyberattack recognition while remaining transparent enough for practical cybersecurity management. The results also show that communication-structure variables provide strong discriminatory power and that replay-type activity remains more difficult to separate than benign or denial-of-service behavior. The proposed framework therefore contributes a reproducible analytical design and a managerial reading of cyberattack classification for UAV operations.
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References
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