Guimaraes, Raniere RochaPassos Jr, Leandro A.Holanda Filho, RaimirAlbuquerque, Victor Hugo C. deRodrigues, Joel J. P. C.Komarov, Mikhail M.Papa, Joao Paulo [UNESP]2019-10-052019-10-052019-03-01Ieee Network. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 33, n. 2, p. 126-131, 2019.0890-8044http://hdl.handle.net/11449/186701Distinguishing outliers from normal data in wireless sensor networks has been a big challenge in the anomaly detection domain, mostly due to the nature of the anomalies, such as software or hardware failures, reading errors or malicious attacks, just to name a few. In this article, we introduce an anomaly detection-based OPF classifier in the aforementioned context. The results are compared against one-class support vector machines and multivariate Gaussian distribution. Additionally, we also propose to employ meta-heuristic optimization techniques to fine-tune the OPF classifier in the context of anomaly detection in wireless sensor networks.126-131engArtigo10.1109/MNET.2018.1800151WOS:000463036200018Acesso aberto