Manipulating hidden-Markov-model inferences by corrupting batch data
William N. Caballero Jose Manuel Camacho Tahir Ekin Roi Naveiro
Time-series models typically assume untainted and legitimate streams of data.
However, a self-interested adversary may have incentive to corrupt this data,
thereby altering a decision maker's inference. Within the broader field of
adversarial machine learning, this research provides a novel, probabilistic
perspective toward the manipulation of hidden Markov model inferences via
corrupted data. In particular, we provision a suite of corruption problems for
filtering, smoothing, and decoding inferences leveraging an adversarial risk
analysis approach. Multiple stochastic programming models are set forth that
incorporate realistic uncertainties and varied attacker objectives. Three
general solution methods are developed by alternatively viewing the problem
from frequentist and Bayesian perspectives. The efficacy of each method is
illustrated via extensive, empirical testing. The developed methods are
characterized by their solution quality and computational effort, resulting in
a stratification of techniques across varying problem-instance architectures.
This research highlights the weaknesses of hidden Markov models under
adversarial activity, thereby motivating the need for robustification
techniques to ensure their security.