Intelligent Network Security Monitoring Based on Optimum-Path Forest Clustering
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Ieee-inst Electrical Electronics Engineers Inc
Institute of Electrical and Electronics Engineers (IEEE)
Institute of Electrical and Electronics Engineers (IEEE)
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Distinguishing 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.
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Ieee Network. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 33, n. 2, p. 126-131, 2019.
