First-order bifurcation detection for dynamic complex networks

TL;DR

This paper introduces a method combining network centrality and von Neumann graph entropy to detect bifurcation events in dynamic networks, demonstrated through cyber intrusion detection.

Contribution

It proposes a novel approach that uses centrality features and an efficient estimator of VNGE to identify structural changes in dynamic networks.

Findings

Effective bifurcation detection in real-world networks

Reduced computational complexity of VNGE estimation

Successful application to cyber intrusion detection

Abstract

In this paper, we explore how network centrality and network entropy can be used to identify a bifurcation network event. A bifurcation often occurs when a network undergoes a qualitative change in its structure as a response to internal changes or external signals. In this paper, we show that network centrality allows us to capture important topological properties of dynamic networks. By extracting multiple centrality features from a network for dimensionality reduction, we are able to track the network dynamics underlying an intrinsic low-dimensional manifold. Moreover, we employ von Neumann graph entropy (VNGE) to measure the information divergence between networks over time. In particular, we propose an asymptotically consistent estimator of VNGE so that the cubic complexity of VNGE is reduced to quadratic complexity that scales more gracefully with network size. Finally, the effectiveness of our approaches is demonstrated through a real-life application of cyber intrusion detection.