IEEE Transactions on Software Engineering - Special issue on computer security and privacy
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Machine Learning - Special issue on learning with probabilistic representations
Intrusion detection in wireless ad-hoc networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Tutorial on maximum likelihood estimation
Journal of Mathematical Psychology
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Distributed regression: an efficient framework for modeling sensor network data
Proceedings of the 3rd international symposium on Information processing in sensor networks
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
LAD: Localization Anomaly Detection forWireless Sensor Networks
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Papers - Volume 01
AINA '07 Proceedings of the 21st International Conference on Advanced Networking and Applications
Mote-Based Online Anomaly Detection Using Echo State Networks
DCOSS '09 Proceedings of the 5th IEEE International Conference on Distributed Computing in Sensor Systems
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We investigate the problem of identifying anomalies in monitoring critical gas concentrations using a sensor network in an underground coal mine. In this domain, one of the main problems is a provision of mine specific anomaly detection, with cyclical (moving) instead of flatline (static) alarm threshold levels. An additional practical difficulty in modelling a specific mine is the lack of fully labelled data of normal and abnormal situations. We present an approach addressing these difficulties based on echo state networks learning mine specific anomalies when only normal data is available. Echo state networks utilize incremental updates driven by new sensor readings, thus enabling a detection of anomalies at any time during the sensor network operation. We evaluate this approach against a benchmark -- Bayesian network based anomaly detection, and observe that the quality of the overall predictions is comparable to the benchmark. However, the echo state networks maintain the same level of predictive accuracy for data from multiple sources. Therefore, the ability of echo state networks to model dynamical systems make this approach more suitable for anomaly detection and predictions in sensor networks.