A Note on Implementing Recurrence Quantification Analysis for Network Anomaly Detection
This paper deal with the network anomaly detection, based on the analysis of non-stationary properties that occur in the aggregated IP traffic flows. We use recurrence quantification analysis (RQA), a mathematical nonlinear technique to achieve this task. The objective is to model the standard network traffic and report any deviation from it. We create a baseline from which we derive the RQA parameters. Using these parameters we explore the hidden recurrence patterns in the network traffic. Further, the detection is analysed using the support vector machine to classify the deviations from the regular traffic. Experiments are conducted on Vellore Institute of Technology University campus network traffic data to validate the model.
Defence Science Journal, 2012, 62(2), pp.112-116, DOI:http://dx.doi.org/10.14429/dsj.62.1171
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