Robust statistical methods for securing wireless localization in sensor networks
Robust statistical methods for securing wireless localization in sensor networks
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DOI：
10.1109/ipsn.2005.1440903
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发表时间：
2005-04
期刊：
影响因子：
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通讯作者：
Zang Li;W. Trappe;Yanyong Zhang;B. Nath
中科院分区：
文献类型：
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作者：
Zang Li;W. Trappe;Yanyong Zhang;B. Nath
正在开发的许多传感器应用需要无线设备的定位，并且已经开发了定位方案来满足该需求。然而，随着基于位置的服务变得越来越普遍，本地化基础设施将成为恶意攻击的目标。这些攻击将不是传统的安全威胁，而是对本地化方案提供可信位置信息的能力产生不利影响的威胁。本文确定了一个列表的攻击是唯一的定位算法。由于这些攻击在本质上是多样的，并且可能存在许多可以绕过传统安全对策的不可预见的攻击，因此期望改变底层定位算法以对故意破坏的测量结果是鲁棒的。在本文中，我们开发了强大的统计方法，使本地化攻击容忍。我们研究了两大类本地化：三角测量和基于RF的指纹识别方法。对于基于三角定位，我们提出了一种自适应最小二乘和最小中位平方位置估计，具有最小二乘在没有攻击的情况下的计算优势，并能够切换到一个强大的模式时，受到攻击。我们引入鲁棒性指纹定位通过使用基于中值的距离度量。最后，我们评估我们的鲁棒定位方案在不同的威胁条件下。
Many sensor applications are being developed that require the location of wireless devices, and localization schemes have been developed to meet this need. However, as location-based services become more prevalent, the localization infrastructure will become the target of malicious attacks. These attacks will not be conventional security threats, but rather threats that adversely affect the ability of localization schemes to provide trustworthy location information. This paper identifies a list of attacks that are unique to localization algorithms. Since these attacks are diverse in nature, and there may be many unforeseen attacks that can bypass traditional security countermeasures, it is desirable to alter the underlying localization algorithms to be robust to intentionally corrupted measurements. In this paper, we develop robust statistical methods to make localization attack-tolerant. We examine two broad classes of localization: triangulation and RF-based fingerprinting methods. For triangulation-based localization, we propose an adaptive least squares and least median squares position estimator that has the computational advantages of least squares in the absence of attacks and is capable of switching to a robust mode when being attacked. We introduce robustness to fingerprinting localization through the use of a median-based distance metric. Finally, we evaluate our robust localization schemes under different threat conditions.