Hiding clusters in adversarial settings

In adversarial settings, records associated with those who want to conceal their existence or activities tend to be unusual because of their illicit status; but not too unusual because of efforts to make them as normal as possible. Clusters of such records will not be single outliers or even outlying clusters, but rather small clusters on the fringes of normal clusters. Such structures are undetectable by many mainstream clustering algorithms, for example those based on distance and convexity. We show that even more sophisticated clustering algorithms are easily subverted by the addition of only a few carefully chosen records. Robust clustering in adversarial settings will require the development of more sophisticated algorithms tailored to this domain.