AI 中文总结
针对联网售后设备的安全问题，提出ZT FL CADE架构，结合零信任、联邦学习与对抗性验证，实验提升维护与入侵检测F1值，延迟符合要求，可联合评估零信任与联邦学习。
AI 中文摘要
联网售后设备将车辆诊断、维修工作流程及空中软件维护延伸至原始设备制造商边界之外，但其异构所有权与长使用寿命使传统 perimeter 安全复杂化。本文开发了面向联网售后设备的零信任联邦学习（Zero Trust Federated Learning for Connected Aftermarket Devices，ZT FL CADE），这是一种边缘学习架构，结合了设备级访问控制、隐私保护型联邦学习及对抗性验证，用于空中更新与预测性维护决策。评估使用了一个单一合成数据集，包含来自240台设备、12个供应商域、180天运行的144000个遥测窗口。特征包括总线熵、更新延迟、认证时长、签名重试次数、环境信号、故障码率、丢包率、漂移、里程数及信任分数，目标为维护风险、更新入侵及访问动作。ZT FL CADE训练本地时序模型，聚合隐私受限的更新，根据行为与更新完整性证据对每台设备评分，并对更新请求进行允许、挑战或隔离动作的路由。合成实验将维护风险F1值从FedAvg的0.837提升至0.883，将入侵F1值从0.856提升至0.897，且当20%的选定客户端为对抗性时，保留0.842的入侵召回率。平均访问决策延迟保持在44毫秒，低于模拟中使用的100毫秒操作预算。结果未确立现场验证，但表明零信任策略执行与联邦学习可作为联合的售后安全控制进行评估，而非分开评估。
英文摘要
Connected aftermarket devices extend vehicle diagnostics, repair workflows, and over-the-air software maintenance beyond original equipment manufacturer boundaries, yet their heterogeneous ownership and long service life complicate conventional perimeter security. This paper develops Zero Trust Federated Learning for Connected Aftermarket Devices (ZT FL CADE), an edge-learning architecture that combines device-level access control, privacy-preserving federated learning, and adversarial validation for over-the-air update and predictive maintenance decisions. The evaluation uses a single synthetic dataset of 144,000 telemetry windows from 240 devices, 12 vendor domains, and 180 days of operation. Features include bus entropy, update latency, attestation age, signature retries, environmental signals, fault-code rates, packet loss, drift, mileage, and trust score, with targets for maintenance risk, update intrusion, and access action. ZT FL CADE trains local temporal models, aggregates privacy-bounded updates, scores each device against behavioral and update integrity evidence, and routes update requests to allow, challenge, or quarantine actions. Synthetic experiments improve maintenance risk F1 from 0.837 for Fed Avg to 0.883, improve intrusion F1 from 0.856 to 0.897, and preserve 0.842 intrusion recall when 20 percent of selected clients are adversarial. Mean access-decision latency remains 44 MS, below the 100 MS operational budget used in the simulation. The results do not establish field validation, but they indicate that zero-trust policy enforcement and federated learning can be evaluated jointly rather than as separate aftermarket security controls. Index Terms Zero trust architecture, federated learning, connected aftermarket devices, over-the-air updates, adversarial machine learning, edge artificial intelligence, predictive maintenance, automotive cybersecurity
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