Abstract
Due to frequent changes in system services, the amount of data collected in a certain state is insufficient, which causes problems such as insufficiency of normal sample data, scarcity of fault sample data, and lack of prior knowledge. Aiming at the problem of anomaly detection of small sample operation and maintenance data lacking negative samples, this paper proposes an operation and maintenance data anomaly detection method based on one-class learning, which uses SVDD (support vector data description) method to eliminate abnormal data in the collected operation and maintenance data. Then, we can better analyze the subsequent data. The experiments show that the proposed method is reasonable and effective.
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