Anomaly detection method for sensor network data streams based on sliding window sampling and optimized clustering. (October 2019)

• Record Type: Journal Article
• Authors: Lin, Ling; Su, Jinshan
• Is Part Of: Safety science. Volume 118(2019)
• Page Start: 70
• Page End: 75
• Publication Date: 2019-10
• Subjects: Data stream sampling -- Dimension cluster -- Maximum entropy principle -- Clustering -- Anomaly detection; Industrial accidents -- Periodicals; Accident Prevention -- Periodicals; Safety -- Periodicals; Travail -- Accidents -- Périodiques; 363.11
• DOI: 10.1016/j.ssci.2019.04.047
• Languages: English
• ISSNs: 0925-7535

Abstract

Highlights: Source of the abnormal data detection is proposed. Optimized clustering methodology is adopted. Results show that accuracy of data anomaly detection has improved. Abstract: When detecting abnormal data in the sensor network data stream, it is necessary to accurately obtain the source of the abnormal data. The traditional data stream clustering algorithm has the disadvantages of large clustering information loss and low accuracy. Therefore, this paper proposes a sensor network data stream anomaly detection method based on optimized clustering. Firstly, the proposed sampling algorithm is used to sample the data stream. The sampling result is used as a sample set. Use dynamic data histogram to divide the data dimension into different dimension groups, calculate the maximum entropy division dimension space cluster of each dimension, and aggregate the data of the same dimension cluster into the micro cluster. The abnormality detection of the data stream is realized by comparing the information entropy size of the micro cluster and its distribution characteristics. The experimental results show that the proposed algorithm can improve the accuracy and effectiveness of data stream anomaly detection.