AirCatch: 有效追踪基于标签的跟踪器
AirCatch: Effectively tracing advanced tag-based trackers
AI总结：
AirCatch 是一种低成本的被动检测系统，通过分析载波频率偏移特征空间中的稳定模拟印记，有效追踪基于标签的恶意跟踪器。
摘要：
基于标签的跟踪生态系统帮助用户寻找丢失的物品，但也可以被用于 unwanted 的跟踪和跟踪。现有的协议驱动的防御措施和先前的学术解决方案大多假设稳定的标识符或可预测的 beaconing。然而，基于标识符的防御措施在面对主动的恶意跟踪器时会彻底崩溃，这些恶意跟踪器会积极地旋转标识符。我们提出了 AirCatch，一种被动检测系统，它利用了物理层的限制：虽然逻辑标识符可以以任意快的速度变化，但发射机的模拟印记保持稳定，并在载波频率偏移（CFO）特征空间中重新出现为一个紧凑且持续占用的区域。AirCatch 在三个轴上推动了现有技术的边界：（i）一种新的、调制感知的 CFO 指纹，它增强了包级 CFO 与内容无关的 CFO 组件，放大了设备的独特性；（ii）一种基于高核心密度和持续性的新的跟踪检测算法，它在通过每个标识符分割时具有抗污染和抗逃避能力；（iii）一种超低成本的接收器，一种大约 10 美元的 BLE SDR，名为 BlePhasyr，由商用组件构建，使基于 RF 指纹的检测在资源受限的部署中变得实用。我们评估了 AirCatch 在 Apple、Google、Tile 和 Samsung 标签家族上的多小时捕获，系统性地通过场景生成器在传输和旋转周期的网格上压力测试逃避，验证了在多样化的现实世界移动轨迹中，包括家庭和办公室通勤、公共交通、汽车旅行和机场旅程的同时，扫过背景标签密度。在这些压力测试中，AirCatch 实现了零假阳性，并在广泛的对抗性配置和环境中实现了早期检测，仅在极低速率的极端情况下才逐渐退化，这些极端情况也减少了攻击者的效用。
英文摘要：
Tag-based tracking ecosystems help users locate lost items, but can be leveraged for unwanted tracking and stalking. Existing protocol-driven defenses and prior academic solutions largely assume stable identifiers or predictable beaconing. However, identifier-based defenses fundamentally break down against advanced rogue trackers that aggressively rotate identifiers. We present AirCatch, a passive detection system that exploits a physical-layer constraint: while logical identifiers can change arbitrarily fast, the transmitter's analog imprint remains stable and reappears as a compact and persistently occupied region in Carrier Frequency Offset (CFO) feature space. AirCatch advances the state of the art along three axes: (i) a novel, modulation-aware CFO fingerprint that augments packet-level CFO with content-independent CFO components that amplify device distinctiveness; (ii) a new tracking detection algorithm based on high core density and persistence that is robust to contamination and evasion through per-identifier segmentation; and (iii) an ultra-low-cost receiver, an approximately 10 dollar BLE SDR named BlePhasyr, built from commodity components, that makes RF fingerprinting based detection practical in resource-constrained deployments. We evaluate AirCatch across Apple, Google, Tile, and Samsung tag families in multi-hour captures, systematically stress-test evasion using a scenario generator over a grid of transmission and rotation periods, and validate in diverse real-world mobility traces including home and office commutes, public transport, car travel, and airport journeys while sweeping background tag density. Across these stress tests, AirCatch achieves no false positives and early detection over a wide range of adversarial configurations and environments, degrading gracefully only in extreme low-rate regimes that also reduce attacker utility.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
