Risk-Aware Cyberattack Analytics for Unmanned Aerial
Vehicle Communications: A Publication-Ready Gradient-
Boosting Framework
Andino Maseleno1,∗, Aa Hubur2
1Institut Bakti Nusantara, Lampung, Indonesia
2Universitas Trisakti, Jakarta, Indonesia
Emails: andino.maseleno@ibnus.ac.id; Maa.hubur@trisakti.ac.id
Abstract
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.
Keywords: UAV cybersecurity; Cyberattack analytics; Gradient boosting; Intrusion detection;
Multiclass classification; Interpretable security