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 finetune the OPF classifier in the context of anomaly detection in wireless sensor networks.

中文翻译：

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基于最优路径森林聚类的智能网络安全监控


将无线传感器网络中的异常值与正常数据区分开来一直是异常检测领域的一大挑战，这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等。在本文中，我们在上述背景下介绍了一种基于异常检测的 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异常检测的背景下采用元启发式优化技术来微调 OPF 分类器。