论文标题

部分可观测时空混沌系统的无模型预测

What Your Wearable Devices Revealed About You and Possibilities of Non-Cooperative 802.11 Presence Detection During Your Last IPIN Visit

论文作者

Bravenec, Tomas, Torres-Sospedra, Joaquín, Gould, Michael, Fryza, Tomas

论文摘要

储层计算是预测湍流的有力工具,其简单的架构具有处理大型系统的计算效率。然而,其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间,然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用,该系统模拟了湍流的若干特征。我们表明,使用径向基函数作为非线性投影器,即使只有部分观测并且不知道控制方程,也能稳健地捕捉复杂的系统非线性。最后,我们表明,当测量稀疏、不完整且带有噪声,甚至控制方程变得不准确时,我们的网络仍然可以产生相当准确的预测,从而为实际湍流系统的无模型预测铺平了道路。

The focus on privacy-related measures regarding wireless networks grew in last couple of years. This is especially important with technologies like Wi-Fi or Bluetooth, which are all around us and our smartphones use them not just for connection to the internet or other devices, but for localization purposes as well. In this paper, we analyze and evaluate probe request frames of 802.11 wireless protocol captured during the 11th international conference on Indoor Positioning and Indoor Navigation (IPIN) 2021. We explore the temporal occupancy of the conference space during four days of the conference as well as non-cooperatively track the presence of devices in the proximity of the session rooms using 802.11 management frames, with and without using MAC address randomization. We carried out this analysis without trying to identify/reveal the identity of the users or in any way reverse the MAC address randomization. As a result of the analysis, we detected that there are still many devices not adopting MAC randomization, because either it is not implemented, or users disabled it. In addition, many devices can be easily tracked despite employing MAC randomization.

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