Using Complex Network Theory for Temporal Locality in Network Traffic Flows

TL;DR

This paper introduces the Temporal Locality Complex Network (TLCN) model to analyze, monitor, and visualize network traffic flows, revealing structural patterns and anomalies even when traffic is obfuscated or encrypted.

Contribution

The paper proposes the TLCN model for network traffic analysis, demonstrating its effectiveness in characterizing flow interactions and detecting anomalies through complex network analysis techniques.

Findings

Weak interaction flows form small-world TLCNs

Strong interaction flows form scale-free TLCNs

TLCN structures reveal attack patterns and evolution

Abstract

Monitoring the interaction behaviors of network traffic flows and detecting unwanted Internet applications and anomalous flows have become a challenging problem, since many applications obfuscate their network traffic flow using unregistered port numbers or payload encryption. In this paper, the temporal locality complex network model--TLCN is proposed as a way to monitor, analyze and visualize network traffic flows. TLCNs model the interaction behaviors of large-scale network traffic flows, where the nodes and the edges can be defined to represent different flow levels and flow interactions separately. Then, the statistical characteristics and dynamic behaviors of the TLCNs are studied to represent TLCN's structure representing ability to the flow interactions. According to the analysis of TLCN statistical characteristics with different Internet applications, we found that the weak interaction flows prefer to form the small-world TLCN and the strong interaction flows prefer to the scale-free TLCN. In the studies of anomaly behaviors of TLCNs, the network structure of attacked TLCNs can have a remarkable feature for three attack patterns, and the evolution of TLCNs exhibits a good consistency between TLCN structure and attack process. With the introduction of TLCNs, we are able to harness a wealth of tools and graph modeling techniques from a diverse set of disciplines.