Improving network traffic analysis by foreseeing data-packet-flow with hybrid fuzzy-based model prediction

Forecast of the flow of data packets on a computer network gives valuable information about the change of data-packet-flow to the website at the next upcoming period, which is a way to enhance the capability of network traffic analysis. Thousands of web-smart businesses depend on network traffic analysis to improve network conversions, reduce marketing costs, facilitate network optimization, speed-up network monitoring and provide a higher level of service to their customers and partners. In this study, an intelligent-based hybrid model prediction is introduced for foreseeing data-packet-flow on a network. This is to combine adaptive neuro-fuzzy inference system (ANFIS) with nonlinear generalized autoregressive conditional heteroscedasticity (NGARCH), tuned optimally by adaptive support vector regression (ASVR). The hybrid model is chosen for resolving the problems of the overshoot and volatility clustering simultaneously so as to improve the predictive accuracy and we denote it as ASVR-ANFIS/NGARCH in this paper. Once we start on the scheme of foreseeing data-packet-flow on a network, the throughput ratio of foreseeing and non-foreseeing data-packet-flow is increased roughly up to 20%. We thereby drew the conclusion that the proposed scheme above can aid webmaster to improve network bandwidth allocation effectively and efficiently and then help web analytics to optimize their website, maximize online marketing conversions, and lead campaign tracking.