用区块链和联邦学习提升无人机网络的安全性与能效。
SkyTrust: Blockchain-Enhanced UAV Security for NTNs with Dynamic Trust and Energy-Aware Consensus
- 基于动态信任评分与能耗共识，实时评估无人机节点可信度。
- 实现94%的信任预测准确率和96%的恶意无人机检测率。
- 适合6G时代分布式智能与可持续安全需求的无人机网络。
基于无人机作为基站的非地面网络（NTNs）因分布性和动态性，极易遭受安全攻击，尤其面临恶意节点威胁。本文提出一种动态信任评分调整机制与能耗感知共识（DTSAM-EAC），通过权限型Hyperledger Fabric区块链与联邦学习（FL）结合，实现隐私保护下的信任评估。信任评分通过历史信任、当前行为和能量贡献加权聚合持续更新，增强系统对网络变化的适应能力。能耗感知共识优先选择剩余能量更高的无人机参与区块验证，提升资源受限环境下的效率。带信任加权的联邦学习进一步增强了全局信任模型的鲁棒性。仿真结果表明，该框架在隐私性、能效和可靠性上均优于集中式与静态信任基线方案，信任评分预测准确率达94%，恶意无人机检测率达96%，符合6G对分布式智能与可持续性的要求，是一种高效可扩展的无人机网络安全保障方案。
原文摘要 · Abstract (English)
Non-Terrestrial Networks (NTNs) based on Unmanned Aerial Vehicles (UAVs) as base stations are extremely susceptible to security attacks due to their distributed and dynamic nature, which makes them vulnerable to rogue nodes. In this paper, a new Dynamic Trust Score Adjustment Mechanism with Energy-Aware Consensus (DTSAM-EAC) is proposed to enhance security in UAV-based NTNs. The proposed framework integrates a permissioned Hyperledger Fabric blockchain with Federated Learning (FL) to support privacy-preserving trust evaluation. Trust ratings are updated continuously through weighted aggregation of past trust, present behavior, and energy contribution, thus making the system adaptive to changing network conditions. An energy-aware consensus mechanism prioritizes UAVs with greater available energy for block validation, ensuring efficient use of resources under resource-constrained environments. FL aggregation with trust-weighting further increases the resilience of the global trust model. Simulation results verify the designed framework achieves 94\% trust score prediction accuracy and 96\% rogue UAV detection rate while outperforming centralized and static baselines of trust-based solutions on privacy, energy efficiency, and reliability. It complies with 6G requirements in terms of distributed intelligence and sustainability and is an energy-efficient and scalable solution to secure NTNs.
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