The complexity of a network nowadays and its increasingly amount of traffic data has contributed to the occurrence of problems and anomalies. A traffic characterization, called Digital Signature for Network Segment using Flow Analysis (DSNSF) is important to help Network Management in avoiding these problems. We propose two methods to generate a digital signature capable of describing the traffic behavior. For this purpose, we used the statistical method Principal Component Analysis (PCA) and the clustering algorithm K-Means. The resulting DSNSFs are then submitted to testing with real data to evaluate its precision.
This paper presents the use of two methods for creating a digital signature of a network segment based on flow analysis (DSNSF), which can be defined as a traffic characterization of a network segment. This characterization is achieved through the statistical forecasting method HoltWinters. Furthermore, a modification is proposed to this traditional method aiming towards better results in its use for creating DSNSF. The data used in the tests are flows collected through NetFlow v9. The results demonstrate that the proposed amendment on the Holt-Winters method showed better results creating DSNSF than the traditional method.
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