SPINS: security protocols for sensor networks
Wireless Networks
Random Key Predistribution Schemes for Sensor Networks
SP '03 Proceedings of the 2003 IEEE Symposium on Security and Privacy
Security in wireless sensor networks
Communications of the ACM - Wireless sensor networks
Statistical location detection with sensor networks
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
Online outlier detection in sensor data using non-parametric models
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
LEAP+: Efficient security mechanisms for large-scale distributed sensor networks
ACM Transactions on Sensor Networks (TOSN)
Spatio-temporal network anomaly detection by assessing deviations of empirical measures
IEEE/ACM Transactions on Networking (TON)
Robust and distributed stochastic localization in sensor networks: Theory and experimental results
ACM Transactions on Sensor Networks (TOSN)
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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 “normal” 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. A series of simulation results, some with real sensor data, validate the effectiveness of the proposed anomaly detection algorithms.