Ghaleb M., Riaz Z., Saleh R. A., Saleh Z., Althamary I., Khan S. A., ...Daha Fazla
MATHEMATICS, cilt.14, sa.20, ss.1-39, 2026 (Scopus)
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Yayın Türü:
Makale / Tam Makale
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Cilt numarası:
14
Sayı:
20
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Basım Tarihi:
2026
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Doi Numarası:
10.3390/math14203817
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Dergi Adı:
MATHEMATICS
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Derginin Tarandığı İndeksler:
Academic Search Ultimate (EBSCO), Scopus, Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest), Aerospace Database, zbMATH, Directory of Open Access Journals
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Sayfa Sayıları:
ss.1-39
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Kocaeli Üniversitesi Adresli:
Evet
Özet
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.