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PASID: Exploiting Indoor mmWave Deployments for Passive Intrusion Detection

机译：PASID：利用室内mmWave部署进行被动入侵检测

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摘要

As 5G deployments start to roll-out, indoor solutions are increasingly pressed towards delivering a similar user experience. Wi-Fi is the predominant technology of choice indoors and major vendors started addressing this need by incorporating the mmWave band to their products. In the near future, mmWave devices are expected to become pervasive, opening up new business opportunities to exploit their unique properties.In this paper, we present a novel PASsive Intrusion Detection system, namely PASID, leveraging on already deployed indoor mmWave communication systems. PASID is a software module that runs in off-the-shelf mmWave devices. It automatically models indoor environments in a passive manner by exploiting regular beamforming alignment procedures and detects intruders with a high accuracy. We model this problem analytically and show that for dynamic environments machine learning techniques are a cost-efficient solution to avoid false positives. PASID has been implemented in commercial off-the-shelf devices and deployed in an office environment for validation purposes. Our results show its intruder detection effectiveness (∼99% accuracy) and localization potential (∼ 2 meters range) together with its negligible energy increase cost (∼ 2%).
机译：随着5G部署的开始推出，室内解决方案正越来越多地被迫提供类似的用户体验。 Wi-Fi是室内首选的主要技术，主要供应商开始通过将mmWave频段纳入其产品来满足这一需求。不久的将来，mmWave设备有望普及，从而为利用其独特性能开辟新的商机。在本文中，我们将利用已部署的室内mmWave通信系统，提出一种新颖的PASsive入侵检测系统，即PASID。 PASID是在现成的mmWave设备中运行的软件模块。它通过采用常规的波束成形对准程序，以被动方式自动对室内环境进行建模，并以高精度检测入侵者。我们通过分析对该问题进行建模，并表明在动态环境中，机器学习技术是避免误报的一种经济高效的解决方案。 PASID已在商用现货设备中实现，并已部署在办公环境中以进行验证。我们的结果表明，其入侵者探测效率（〜99％精度）和定位潜力（〜2米范围）以及可忽略的能量增加成本（〜2％）。

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