A Graph Based Approach Toward Network Forensics Analysis

Abstract

In this article we develop a novel graph-based approach toward network forensics analysis. Central to our approach is the evidence graph model that facilitates evidence presentation and automated reasoning. Based on the evidence graph, we propose a hierarchical reasoning framework that consists of two levels. Local reasoning aims to infer the functional states of network entities from local observations. Global reasoning aims to identify important entities from the graph structure and extract groups of densely correlated participants in the attack scenario. This article also presents a framework for interactive hypothesis testing, which helps to identify the attacker's nonexplicit attack activities from secondary evidence. We developed a prototype system that implements the techniques discussed. Experimental results on various attack datasets demonstrate that our analysis mechanism achieves good coverage and accuracy in attack group and scenario extraction with less dependence on hard-coded expert knowledge.