Title:
UnsupervisedAnomalyDetectionBasedOnPrincipalCompon
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Description: Intrusion Detection System in the training process requires a large logo of Jiandushuoju learning, negative effect on their application and promotion. In order to solve the problem, a principal component analysis based on unsupervised anomaly detection method, the principle of the minimum mean square error The main characteristics of the sample under study, after compression and the reciprocal reduction procedure to copy the sample information as possible to the mean square error of the difference according to detect anomalies. Construction of the simulation system has been proved, not based on principal component analysis anomaly detection methods to monitor without the participation of experts in early detection of invasive cases, experimental results show its effectiveness.
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