IPv6 networks are at present under vast deployment in production networks as well as in the Internet. Tasks such as resource reservation, capacity planning, and effective security deployments necessitate the understanding of IPv6 flow behavior in the nodes as well as in the network. To accomplish the understanding of IPv6 flow behavior, a tool that generates application and attack flows based on sound mathematical models is essential. Since different flows have different characteristics, the model parameters for a particular flow feature are to be assigned with appropriate values. In our tool, we have identified five flow features for characterizing the flows. They are, namely, inter departure time (IDT), packet size (PS), flow count (FC), flow volume (FV), and flow duration (FD). Random sampling from distributions such as Exponential, Pareto, Poisson, Cauchy, Gamma, Student, Wei-bull, and Log Normal are considered. IPv6 TCP and UDP packets are constructed and transmitted for the flows according to the feature values received from the assumed distribution model. The direct model parameter specification and trace-based model parameter learning are incorporated in our tool. Using our tool, we have analyzed the node behavior for IDT feature over different hardware platforms to IPv6 flows. We have presented the IDT analysis and modeled the bit rate using a three parameter function from the experimental measurements. The parameters estimated for different platforms to this model are also reported. © Springer India 2014.