An anomaly detection algorithm based on K-means and BP neural network in wireless sensor networks

Jun Yuan; Xingfeng Guo; Houfan Xiang; Zican Hu; Bin Chen

doi:10.1117/12.2538333

14 February 2020 An anomaly detection algorithm based on K-means and BP neural network in wireless sensor networks

Jun Yuan, Xingfeng Guo, Houfan Xiang, Zican Hu, Bin Chen

Proceedings Volume 11430, MIPPR 2019: Pattern Recognition and Computer Vision; 114300Z (2020) https://doi.org/10.1117/12.2538333
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

Abstract

In recent years, with the development of wireless sensor networks(WSN), it has been applied in more and more areas. However, anomaly detection has been always the hot topic in WSN. In order to solve the above problem, this paper proposes an anomaly detection algorithm which is based on the K-means clustering and BP neural network algorithms. This algorithm firstly employs the K-means clustering algorithm classify and mark the collected original sample data as anomaly and normal. Based on the above tagged data, it then uses the BP neural network algorithm train the classification model and realize the on-line detection of anomaly data. Finally, relevant experiments on virtual and actual sensor databases show that our algorithm can achieve a high outlier detection rate while the false alarm rate is low. In addition, because K-means clustering algorithm is an unsupervised classification method, our algorithm is suitable for different WSN applications scene.

Citation Download Citation

Jun Yuan, Xingfeng Guo, Houfan Xiang, Zican Hu, and Bin Chen "An anomaly detection algorithm based on K-means and BP neural network in wireless sensor networks", Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114300Z (14 February 2020); https://doi.org/10.1117/12.2538333

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