Data driven hyperparameter optimization of one-class support vector machines for anomaly detection in wireless sensor networks

One-class support vector machines (OCSVM) have been recently applied to detect anomalies in wireless sensor networks (WSNs). Typically, OCSVM is kernelized by radial bais functions (RBF, or Gausian kernel) whereas selecting Gaussian kernel hyperparameter is based upon availability of anomalies, which is rarely applicable in practice. This article investigates the application of OCSVM to detect anomalies in WSNs with data-driven hyperparameter optimization. Specifically, the information of the farthest and the nearest neighbors of each sample is used to construct the objective cost instead of labeling based metrics such as geometric mean accuracy (G-mean) or area under the receiver operating characteristic (AUROC). The efficiency of this method is illustrated over the IBRL dataset whereas the resulting estimated boundary as well as anomaly detection performance are comparable with existing methods.

I. Introduction

WSNs are composed of a large number of sensor nodes that can communicate the information gathered from a monitored field through wireless links to monitor physical or environmental conditions, such as temperature, vibration, pressure, motion, etc. and to cooperatively pass their data throughout the network. WSNs are an emerging and very interesting technology applied to different applications including industrial processes, monitoring and control, machine health monitoring, healthcare applications and traffic control. The fourth industrial revolution will make WSNs an integral part of our lives, more so than the present-day personal computers (see [1]). Due to the deployment of a large number of sensor nodes in hostile environments, data collected by WSNs is often unreliable. This will affect the modeling and scientific reasonable inference. Thus, it is significant that the anomaly of sensor node is detected in order to obtain accurate information, therefore making effective decisions by information gatherered (see, for instance, [2]).