Is Data Clustering in Adversarial Settings Secure?

Understand

Clustering algorithms have been increasingly adopted in security applications to spot dangerous or illicit activities.

• However, they have not been originally devised to deal with deliberate attack attempts that may aim to subvert the clustering process itself.
• Whether clustering can be safely adopted in such settings remains thus questionable.
• In this work we propose a general framework that allows one to identify potential attacks against clustering algorithms, and to evaluate their impact, by making specific assumptions on the adversary's goal, knowledge of the attacked system, and capabilities of manipulating the input data.