802.11 localization algorithms provide the ability to accurately position and track wireless clients thereby enabling location-based services and applications. However, we show that these localization techniques are vulnerable to non-cryptographic attacks where an adversary uses a low-cost directional antenna to appear from the localization algorithm's perspective to be in another arbitrary location of their choosing. The attacker's ability to actively influence where they are positioned is a key distinguishing feature of the directional attack relative to prior localization attacks that use transmit power control to introduce localization errors. We implement a representative set of received signal strength-based localization algorithms and evaluate the attack in a real office building environment. To mitigate the attack's effectiveness, we develop and evaluate an attack detection scheme that offers a high detection rate with few false positives.

Clustering aims to find useful hidden structures in data. In this paper we present a new clustering algorithm that builds upon the consistency method (Zhou, et.al., 2003), a semi-supervised learning technique with the property of learning very smooth functions with respect to the intrinsic structure revealed by the data. Other methods, e.g. Spectral Clustering, obtain good results on data that reveals such a structure. However, unlike Spectral Clustering, our algorithm effectively detects both global and within-class outliers, and the most representative examples in each class. Furthermore, we specify an optimization framework that estimates all learning parameters, including the number of clusters, directly from data. Finally, we show that the learned cluster-models can be used to add previously unseen points to clusters without re-learning the original cluster model. Encouraging experimental results are obtained on a number of real world problems.