发表机构
Texas A&M University（德克萨斯农工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出并实证评估了一种面向Wi-Fi多节点机器人集群的分层零信任框架，集成TPM 2.0硬件信任根、SIEM遥测和IPD时序水印，通过峰度统计完全检测中间人命令注入。
AI 中文摘要
自主无人车对战术任务以及搜索救援、灾难响应等关键任务公共安全行动至关重要。然而，它们对开放无线链路和标准机器人操作系统（ROS 2）中间件的依赖暴露了广泛的网络物理攻击面。这些系统一旦被攻破，可能干扰实时控制回路，导致高风险环境中的任务失败或资产损失。本文提出并实证评估了一个分层、上下文感知的网络安全框架，在Wi-Fi上对多节点机器人集群实施零信任原则。该框架集成了主动硬件信任根（TPM 2.0）、集中式带内和带外SIEM遥测监控（ELK Stack和Kismet），以及一种非加密的包间延迟（IPD）时序水印。在实时ROS 2移动测试平台上，针对多层攻击（OSI层2-5）的评估结果表明，虽然基于流量的过滤器能隔离暴力拒绝服务洪泛，但跟踪嵌入IPD水印的统计样本峰度（K）能完全准确地暴露隐蔽的中间人命令注入，且无载荷开销。
英文摘要
Autonomous unmanned vehicles are vital to tactical missions, mission-critical public-safety operations like search and rescue and disaster response. However, their reliance on open wireless links and standard Robot Operating System (ROS 2) middleware exposes a broad cyber-physical attack surface. A compromise of these systems can disrupt real-time control loops, leading to mission failure or asset loss in high-stakes environments. This paper presents and empirically evaluates a layered, context-aware cybersecurity framework enforcing Zero-Trust principles for a multi-node robotic fleet over Wi-Fi. The framework integrates an active hardware root of trust (TPM 2.0), centralized in-band and out-of-band SIEM telemetry monitoring (ELK Stack and Kismet), and a non-cryptographic Inter-Packet Delay (IPD) timing watermark. Evaluated on a live ROS 2 mobile testbed under multi-layer exploits (OSI Layers 2-5), results demonstrate that while volume-based filters isolate brute denial-of-service floods, tracking the statistical sample kurtosis (K) of the embedded IPD watermark exposes stealthy Man-in-the-Middle command injections with complete detection accuracy without payload overheads.
