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Sen. Netw."],"published-print":{"date-parts":[[2010,8]]},"abstract":"<jats:p>We seek to detect statistically significant temporal or spatial changes in either the underlying process the sensor network is monitoring or in the network operation itself. These changes may point to faults, adversarial threats, misbehavior, or other anomalies that require intervention. To that end, we introduce a new statistical anomaly detection framework that uses Markov models to characterize the \u201cnormal\u201d behavior of the sensor network. We develop a series of Markov models, including tree-indexed Markov chains which can model its spatial structure. For each model, an anomaly-free probability law is estimated from past traces. We leverage large deviations techniques to develop optimal anomaly detection rules for each corresponding Markov model, assessing whether its most recent empirical measure is consistent with the anomaly-free probability law. 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