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Data Stream Subspace Clustering for Anomalous Network Packet Detection

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Figure

Figure 1. Expand cluster method to create preference wei- ghted subspace clusters.
Figure 2. StreamPreDeCon algorithm for anomalous packet detection.
Figure 3. positive rate is above 40%. Then when 680, the false positive rates and the sensitivity decrease false positive rate both decrease
Table 2. StreamPreDeCon ID detection results of anomalous packets within the 80 testing data
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