Simulation of Traffic Anomaly Detection in Large Campus Network Subspace
At present,the campus network has many functional partitions,so it is extremely vulnerable to malicious network attacks.To this end,this paper puts forward an algorithm for detecting subspace traffic anomalies in large-scale campus networks.First of all,the collection and topology of campus network traffic were constructed.Ac-cording to IP address segments,the network was divided into five network segments.And then,the NetFlow mechanism was adopted to collect the traffic data of the network segment.Moreover,the principal component analysis method was used to normalize the collected data.After that,principal components were selected from a large number of samples by establishing a sample matrix.Furthermore,the campus network was divided into normal subspace and abnormal subspace,and the traffic measurement vectors were mapped into two subspaces.The points outside the threshold were abnormal points.Finally,the detection was achieved.Experimental results show that the proposed method can accurately detect network traffic anomalies,and achieves ideal detection rate and retention rate.
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