TriTrust: Risk-Aware Three-Factor Trust Fusion for Secure UAV Handover Admission
5 affiliations
- 1Department of Software Engineering, Kocaeli University, İzmit 41001, Türkiye
- 2Interdisciplinary Research Center for Construction and Building Materials, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia
- 3Department of Industrial Engineering, College of Engineering, University of Business and Technology, Jeddah 21361, Saudi Arabia
- 4Interdisciplinary Research Center for Intelligent Secure Systems (IRC-ISS), King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia
- 5College of Applied Computer Sciences, King Saud University, Riyadh 11543, Saudi Arabia
Abstract
Cooperative unmanned aerial vehicle (UAV) swarms require secure transaction-level admission decisions when tasks, relay roles, control privileges, or mission assets are transferred between aircraft. Existing authentication and intrusion-detection mechanisms address complementary aspects of security and motivate integrating a mandatory protocol check with post-authentication behavioral and corroborative assessment. This paper introduces TriTrust, a risk-aware three-factor trust-fusion architecture that enforces a mandatory protocol-admission gate, followed by logistic fusion of traffic-behavior and competence-weighted witness scores for gate-passing candidates. The framework further incorporates validation-fitted score mapping and robust federated maintenance of the witness classifier. Evaluation on a constructed proxy-handover benchmark derived from public UAV cyber-physical records using cyber-condition-derived labels, sampled gate outcomes, and simulated witness views demonstrates that the modeled gate provides the dominant control over false acceptance: removing it increases mean seed-level raw-score false-acceptance rate (FAR) from 0.43% to 9.62%. Across five prespecified resamplings of the same source corpus, the isotonic-mapped full pipeline attains a mean seed-level area under the receiver operating characteristic curve (AUC) of 0.9926, a mean FAR of 0.83%, and a mean false-rejection rate (FRR) of 1.75%. Under 20% scaled sign-reversal poisoning, coordinatewise median aggregation maintains mean witness-classifier accuracy of 0.8552, compared with 0.2920 for sample-size-weighted averaging. Feature-space stress tests further show that false acceptance under learned-input manipulation remains limited by the modeled gate, whereas gate bypass combined with learned-factor compromise produces high false-acceptance rates. These results characterize the contributions and failure boundary of protocol gating, post-gate trust fusion, score mapping, and robust federated maintenance within the constructed benchmark.
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BibTeX
@article{ghaleb2026tritrust,
title = {{TriTrust: Risk-Aware Three-Factor Trust Fusion for Secure UAV Handover Admission}},
author = {Mustafa Ghaleb and Zaafir Riaz and Radhwan A. Saleh and Zead Saleh and Ibrahim Althamary and Sajjad Ahmad Khan and Kerem Küçük and Hamdi Altaheri},
journal = {Mathematics},
year = {2026},
month = oct,
doi = {10.3390/math14203817},
url = {https://www.mdpi.com/2227-7390/14/20/3817},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - TriTrust: Risk-Aware Three-Factor Trust Fusion for Secure UAV Handover Admission
AU - Mustafa Ghaleb
AU - Zaafir Riaz
AU - Radhwan A. Saleh
AU - Zead Saleh
AU - Ibrahim Althamary
AU - Sajjad Ahmad Khan
AU - Kerem Küçük
AU - Hamdi Altaheri
PY - 2026
DA - 2026/10/20
JO - Mathematics
DO - 10.3390/math14203817
UR - https://www.mdpi.com/2227-7390/14/20/3817
AB - Cooperative unmanned aerial vehicle (UAV) swarms require secure transaction-level admission decisions when tasks, relay roles, control privileges, or mission assets are transferred between aircraft. Existing authentication and intrusion-detection mechanisms address complementary aspects of security and motivate integrating a mandatory protocol check with post-authentication behavioral and corroborative assessment. This paper introduces TriTrust, a risk-aware three-factor trust-fusion architecture that enforces a mandatory protocol-admission gate, followed by logistic fusion of traffic-behavior and competence-weighted witness scores for gate-passing candidates. The framework further incorporates validation-fitted score mapping and robust federated maintenance of the witness classifier. Evaluation on a constructed proxy-handover benchmark derived from public UAV cyber-physical records using cyber-condition-derived labels, sampled gate outcomes, and simulated witness views demonstrates that the modeled gate provides the dominant control over false acceptance: removing it increases mean seed-level raw-score false-acceptance rate (FAR) from 0.43% to 9.62%. Across five prespecified resamplings of the same source corpus, the isotonic-mapped full pipeline attains a mean seed-level area under the receiver operating characteristic curve (AUC) of 0.9926, a mean FAR of 0.83%, and a mean false-rejection rate (FRR) of 1.75%. Under 20% scaled sign-reversal poisoning, coordinatewise median aggregation maintains mean witness-classifier accuracy of 0.8552, compared with 0.2920 for sample-size-weighted averaging. Feature-space stress tests further show that false acceptance under learned-input manipulation remains limited by the modeled gate, whereas gate bypass combined with learned-factor compromise produces high false-acceptance rates. These results characterize the contributions and failure boundary of protocol gating, post-gate trust fusion, score mapping, and robust federated maintenance within the constructed benchmark.
SN - 2227-7390
ER -