Attack Graph Generation on HPC Clusters

TL;DR

This paper explores leveraging high performance computing clusters to efficiently generate attack graphs for HPC networks, addressing the challenges of high computational and memory demands in large-scale security analysis.

Contribution

It proposes a HPC-based approach for attack graph generation and evaluates its performance to mitigate speed and memory issues in large networks.

Findings

HPC clusters significantly improve attack graph generation speed.

HPC approach reduces memory consumption during attack graph creation.

Experimental results demonstrate scalability with network size.

Abstract

Attack graphs (AGs) are graphical tools to analyze the security of computer networks. By connecting the exploitation of individual vulnerabilities, AGs expose possible multi-step attacks against target networks, allowing system administrators to take preventive measures to enhance their network's security. As powerful analytical tools, however, AGs are both time- and memory-consuming to be generated. As the numbers of network assets, interconnections between devices, as well as vulnerabilities increase, the size and volume of the resulting AGs grow at a much higher rate, leading to the well-known state-space explosion. In this paper, we propose the use of high performance computing (HPC) clusters to implement AG generators. We evaluate the performance through experiments and provide insights into how cluster environments can help resolve the issues of slow speed and high memory demands in AG generation in a balanced way.