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At present, irrelevant or redundant features in network traffic data occupy a lot of storage and computing resources, reducing the accuracy of network anomaly detection. Aiming at this problem, a many‐objective feature selection model is proposed in this article. The model takes the number of selected feature, false alarm rate, detection rate, precision and accuracy as the optimization objectives,...
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