Anomaly detection based on multi-source heterogeneous data fusion
Keqi Liu, Peng Xu · 2022
In recent years, with the development of the Internet of things, the data collection methods are more and more abundant and the data structure is more complex. Anomaly detection by analyzing multi-source data has become a research hotspot. Multi source data is robust in data accuracy, but there are still some problems, such as difficulty in effective fusion and feature extraction. This paper proposes an anomaly detection method based on multi-source heterogeneous data fusion from the perspective of sequence. The extracted subsequences are mapped into the feature subspace, and then a unified fusion feature space matrix based on multi view is constructed. Finally, an anomaly scoring method based on property attribute fusion is proposed. Anomaly detection is carried out based on fuzzy clustering. The accuracy of the algorithm is verified by many experiments.