Online Traffic Classification Using Granules

Currently, it is still a great challenge to achieve online classification of massive traffic flows under dynamic network environments. Therefore, based on granular computing, an artificial intelligence computing method, which is effective to process missing, incomplete, or noisy data, a novel classification model $\mathrm{M}_{GrC}$ is proposed in this paper. In $\mathrm{M}_{GrC}$, we first define granules for the traffic flow, then explore the correlation between granules, and finally establish the structure granules to differentiate flow types. $\mathrm{M}_{GrC}$ explores the inherent relationship between packets, where the data is no longer isolated, but closely related to each other. So, it can identify the traffic more accurately when compared with the traditional classification methods, which assume the packets to be independent. The experiment results also demonstrate its superior robustness and adaptability in highly variable network environment.