Publication details

Using relational graphs for exploratory analysis of network traffic data

Investor logo


Year of publication 2023
Type Article in Periodical
Magazine / Source Forensic Science International: Digital Investigation
MU Faculty or unit

Institute of Computer Science

Keywords Relational analytics;Network forensics;Visual analytics;Granef;Cybersecurity
Attached files
Description The human brain is designed to perceive the surrounding world as associations. These associations between the individual pieces of information allow us to analyze and categorize new inputs and thus understand them. However, the support for association-based analysis in traditional network analysis tools is only limited or not present at all. These tools are mostly based on manual browsing, filtering, and aggregation, with only basic support for statistical analyses and visualizations for communicating the general characteristics. Yet, it is the relationship diagram that could allow the analysts to get a broader context and reveal the associations hidden in the data. In this paper, we explore the possibilities of relational analysis as a novel paradigm for network forensics. We provide a set of user requirements based on the discussion with domain experts and introduce a novel visual analysis tool utilizing multimodal graphs for modeling relationships between entities from captured packet traces. Finally, we demonstrate the relational analysis process on two use cases and discuss feedback from domain experts.
Related projects:

You are running an old browser version. We recommend updating your browser to its latest version.

More info