Graph analysis of network flow connectivity behaviors

Abstract

Graph-based approaches have been widely employed to facilitate in analyzing network flow connectivitybehaviors, which aim to understand the impacts and patterns of network events. However, existing approaches sufferfrom lack of connectivity-behavior information and loss of network event identification. In this paper, we propose networkflow connectivity graphs (NFCGs) to capture network flow behavior for modeling social behaviors from network entities.Given a set of flows, edges of a NFCG are generated by connecting pairwise hosts who communicate with each other.To preserve more information about network flows, we also embed node-ranking values and edge-weight vectors into theoriginal NFCG. After that, a network flow connectivity behavior analysis framework is present based on NFCGs. Theproposed framework consists of three modules: a graph simplification module based on diversified filtering rules, a graphfeature analysis module based on quantitative or semiquantitative analysis, and a graph structure analysis module basedon several graph mining methods. Furthermore, we evaluate our NFCG-based framework by using real network trafficdata. The results show that NFCGs and the proposed framework can not only achieve good performance on networkbehavior analysis but also exhibit excellent scalability for further algorithmic implementations.