Digital Signature of Network Segment using PCA, ACO and Holt-Winters for network management

The practicality and convenience provided by computer networks made them indispensable, which resulted on their continuous growth both in size and complexity. Traffic monitoring became an arduous work, requiring autonomic mechanisms to describe the network's normal behavior. Thus, we introduce Digital Signature of Network Segment using Flow Analysis (DSNSF) as a mechanism to assist the networks management through traffic characterization. For this purpose, three methods belonging to different groups of algorithms are used: the statistical procedure Principal Component Analysis (PCA), the Ant Colony Optimization (ACO) metaheuristic and Holt-Winters forecasting method. We use real data for traffic characterization and evaluation of the proposed methods. The results demonstrate a good adaptability of these methods, and that generated DSNSFs can characterize the network's traffic effectively.