1. A computer implemented method for detecting cyber physical system behavior, comprising: utilizing one or more processors and associated memory storing one or more programs for execution by the one or more processors, the one or more programs including instructions for: receiving data from a plurality of sensors associated with the cyber physical system; constructing a metrization of the data utilizing a data structuring; determining at least one ensemble and at least one summary variable from the metrized data, wherein the determining includes a symbolic encoding of the metrized data and inferring an automata model utilizing a probabilistic grammatical inference that comprises an ε-Machine Reconstruction statistical machine learning technique that includes describing a system trajectory as a string of symbols and describing system dynamics in terms of shift dynamics of the associated symbol string, wherein the ε-Machine Reconstruction statistical machine learning technique includes at least one of: (a) discovering common subtrees of a string parse tree via a nonparametric Bayesian clustering method including a Dirichlet Process or a Beta Process; or (b) a diffusion map technique; applying a thermodynamic formalism to the at least one summary variable to classify a plurality of system behaviors; identifying the plurality of system behaviors based at least in part on the classified plurality of system behaviors; obtaining, by the one or more processors, a baseline of the system behavior associated with the classified plurality of system behaviors; detecting an anomalous condition based on a deviation of the plurality of system behaviors from the baseline; and generating an output indicating the identified plurality of system behaviors or the anomalous condition.