Abstract
This article addresses the problem of outlier detection for wireless sensor networks. As increasing amounts of observational data are tending to be high-dimensional and large scale, it is becoming increasingly difficult for existing techniques to perform outlier detection accurately and efficiently. Although dimensionality reduction tools (such as deep belief network) have been utilized to compress the high-dimensional data to support outlier detection, these methods may not achieve the desired performance due to the special distribution of the compressed data. Furthermore, because most existed classification methods must solve a quadratic optimization problem in their training stage, they cannot perform well in large-scale datasets. In this article, we developed a new form of classification model called “deep belief network online quarter-sphere support vector machine,” which combines deep belief network with online quarter-sphere one-class support vector machine. Based on this model, we first propose a model training method that learns the radius of the quarter sphere by a sorting method. Then, an online testing method is proposed to perform online outlier detection without supervision. Finally, we compare the proposed method with the state of the arts using extensive experiments. The experimental results show that our method not only reduces the computational cost by three orders of magnitude but also improves the detection accuracy by 3%–5%.
Highlights
With the rapid development of human society, the Internet of Things (IoT) has penetrated every aspect of our culture
To address the above problems, we propose a new form of online outlier detection method DBNOQSSVM, which can accurately and efficiently perform outlier detection for large-scale and highdimensional datasets of wireless sensor network (WSN)
The results show that compared with deep belief network (DBN)-quarter-sphere SVM (QSSVM), DBN-one-class support vector machine (OCSVM), and iForest, the DBN-online QSSVM (OQSSVM) method can reduce the computing time by three orders of magnitude on average and improve the accuracy by 3%–5%
Summary
With the rapid development of human society, the Internet of Things (IoT) has penetrated every aspect of our culture. To address the above problems, we propose a new form of online outlier detection method DBNOQSSVM, which can accurately and efficiently perform outlier detection for large-scale and highdimensional datasets of WSNs. To summarize, this article makes the following contributions to the field of outlier detection for WSNs: We design a new hybrid model DBN-OQSSVM based on DBN and QSSVM, that can process high-dimensional and large-scale data in an unsupervised manner. The related works are reviewed in section ‘‘Related work.’’ The details of the four models referred to in our method, as well as their characteristics, are described in section ‘‘Background and problem formulation.’’ We present our proposed outlier detection method, with an evaluation using experiments on four real datasets, in sections ‘‘Fast outlier detection algorithm for high-dimensional sensor data’’ and ‘‘Evaluation,’’ respectively.
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