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 · 2020
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.