A multilevel hybrid anomaly detection scheme for industrial wireless sensor networks,International Journal of Network Management

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A multilevel hybrid anomaly detection scheme for industrial wireless sensor networks
International Journal of Network Management ( IF 1.5 ) Pub Date : 2020-12-02 , DOI: 10.1002/nem.2144
Shashank Gavel ₁ , Ajay Singh Raghuvanshi ₁ , Sudarshan Tiwari ₂

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Real-time sensing plays an important role in ensuring the reliability of industrial wireless sensor networks (IWSNs). Sensor nodes in IWSNs have inherent limitations that give rise to different anomalies in the network. These anomalies can lead to disastrous and harmful situations or even serious system failures. This article presents a formulation to the design of an anomaly detection scheme for detecting the anomalous node along with the type of anomaly. The proposed scheme is divided into two major parts. First, spatiotemporal correlation within a cluster is obtained for the normal and anomalous behavior of sensor nodes. Second, the multilevel hybrid classifier is used by combining the sequential minimal optimization support vector machine (SMO-SVM) as a binary classifier with optimally pruned extreme learning machine (OP-ELM) as a multiclass classifier for detection of an anomalous node and type of anomalies, respectively. Mahalanobis distance-based lightweight K-Medoid clustering is used to build a new set of training datasets that represents the original training dataset, by significantly reducing the training time of a multilevel hybrid classifier. Results are analyzed using standard WSN datasets. The proposed model shows high accuracy, i.e., 94.79% and detection rate, i.e., 94.6% with a reduced false positive rate as compared to existing hybrid methods.

中文翻译：

一种工业无线传感器网络的多级混合异常检测方案

实时传感在确保工业无线传感器网络 (IWSN) 的可靠性方面发挥着重要作用。IWSN 中的传感器节点具有固有的局限性，会导致网络中出现不同的异常。这些异常可能导致灾难性和有害的情况，甚至严重的系统故障。本文提出了一种用于检测异常节点以及异常类型的异常检测方案的设计公式。拟议的计划分为两个主要部分。首先，为传感器节点的正常和异常行为获得集群内的时空相关性。第二，通过将连续最小优化支持向量机 (SMO-SVM) 作为二元分类器与最佳修剪极限学习机 (OP-ELM) 作为多类分类器相结合，使用多级混合分类器来检测异常节点和异常类型，分别。基于 Mahalanobis 距离的轻量级 K-Medoid 聚类用于构建一组代表原始训练数据集的新训练数据集，通过显着减少多级混合分类器的训练时间。结果使用标准 WSN 数据集进行分析。所提出的模型显示出高精度，即 94.79 基于 Mahalanobis 距离的轻量级 K-Medoid 聚类用于构建一组代表原始训练数据集的新训练数据集，通过显着减少多级混合分类器的训练时间。结果使用标准 WSN 数据集进行分析。所提出的模型显示出高精度，即 94.79 基于 Mahalanobis 距离的轻量级 K-Medoid 聚类用于构建一组代表原始训练数据集的新训练数据集，通过显着减少多级混合分类器的训练时间。结果使用标准 WSN 数据集进行分析。所提出的模型显示出高精度，即 94.79%和检测率，即 94.6 %，与现有混合方法相比，假阳性率降低。

更新日期：2020-12-02

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