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.

中文翻译：

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基于最佳路径森林聚类的智能网络安全监控

在异常检测领域中，将异常值与正常数据区分开是一大挑战，主要是由于异常的性质，例如软件或硬件故障，读取错误或恶意攻击，仅举几例。在本文中，我们在上述情况下介绍了基于异常检测的OPF分类器。将结果与一类支持向量机和多元高斯分布进行比较。此外，我们还建议在无线传感器网络中的异常检测的情况下，采用元启发式优化技术来微调OPF分类器。