Wireless Sensor Networks Anomaly Detection Using Machine Learning: A Survey
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-47715-7_34
Reference42 articles.
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3. Saeedi Emadi, H., Mazinani, S.M.: A novel anomaly detection algorithm using DBSCAN and SVM in wireless sensor networks. Wirel. Personal Commun. 98(2), 2025–2035 (2017). https://doi.org/10.1007/s11277-017-4961-1
4. Bosman, H.H.W.J., Iacca, G., Tejada, A., Wörtche, H.J., Liotta, A.: Spatial anomaly detection in sensor networks using neighborhood information. Inf. Fusion 33, C 41–56 (2017). https://doi.org/10.1016/j.inffus.2016.04.007
5. Feng, Z., Fu, J., Du, D., Li, F., Sun, S.: A new approach of anomaly detection in wireless sensor networks using support vector data description. Int. J. Distribut. Sensor Netw. 13(1) (2017). https://doi.org/10.1177/1550147716686161
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