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Noah L. Schrick 2023-04-22 20:32:45 -05:00
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\begin{thebibliography}{}
\begin{thebibliography}{1}
\bibitem{cook_rage_2018}
K.~Cook, {\em {RAGE}: {The} {Rage} {Attack} {Graph} {Engine}}.
\newblock PhD thesis, The {University} of {Tulsa}, 2018.
\bibitem{li_concurrency_2019}
M.~Li, P.~Hawrylak, and J.~Hale, ``Concurrency {Strategies} for {Attack}
{Graph} {Generation},'' {\em Proceedings - 2019 2nd International Conference
on Data Intelligence and Security, ICDIS 2019}, pp.~174--179, 2019.
\bibitem{li_combining_2019}
M.~Li, P.~Hawrylak, and J.~Hale, ``Combining {OpenCL} and {MPI} to support
heterogeneous computing on a cluster,'' {\em ACM International Conference
Proceeding Series}, 2019.
\bibitem{zhang_boosting_2017}
J.~Zhang, S.~Khoram, and J.~Li, ``Boosting the performance of {FPGA}-based
graph processor using hybrid memory cube: {A} case for breadth first
search,'' {\em FPGA 2017 - Proceedings of the 2017 ACM/SIGDA International
Symposium on Field-Programmable Gate Arrays}, pp.~207--216, 2017.
\bibitem{ainsworth_graph_2016}
S.~Ainsworth and T.~M. Jones, ``Graph prefetching using data structure
knowledge,'' {\em Proceedings of the International Conference on
Supercomputing}, vol.~01-03-June, 2016.
\bibitem{berry_graph_2007}
J.~Berry and B.~Hendrickson, ``Graph {Analysis} with {High} {Performance}
{Computing}.,'' {\em Computing in Science and Engineering}, 2007.
\end{thebibliography}

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\documentclass[conference]{IEEEtran}
\IEEEoverridecommandlockouts
% The preceding line is only needed to identify funding in the first footnote. If that is unneeded, please comment it out.
\usepackage{cite}
\usepackage{amsmath,amssymb,amsfonts}
\usepackage{algorithmic}
@ -44,20 +42,18 @@ peter-hawrylak@utulsa.edu}
\maketitle
\begin{abstract}
This document is a model and instructions for \LaTeX.
This and the IEEEtran.cls file define the components of your paper [title, text, heads, etc.]. *CRITICAL: Do Not Use Symbols, Special Characters, Footnotes,
or Math in Paper Title or Abstract.
Attack graphs and compliance graphs are graphical representations of systems used to analyze their standings in regards to cybersecurity or compliance postures. These graphs are designed to examine all permutations and combinations of changes that can occur to systems, and represent those changes through the topological information of the resulting graph. Due to their exhaustive nature, these graphs are often large-scale. Various efforts have shown success in the parallelization and generation deployment on High-Performance Computing (HPC) systems. Despite the additional compute power that HPC systems provide, these large-scale graphs still posses lengthy runtimes and require large amounts of system memory to fully contain the resulting graph. This work presents a Checkpoint/Restart (C/R) implementation specific to attack and compliance graphs that aims to provide fault-tolerance and memory relief throughout the generation process. This implementation has been designed to be built within the graph generation source code, and provides a lightweight alternative to other C/R implementations. The results indicate that this implementation is successful at providing fault-tolerance and memory relief while having low impact to the generation process and to the source code.
\end{abstract}
\begin{IEEEkeywords}
Attack Graph; Compliance Graph; MPI; High-Performance Computing; Checkpoint/Restart;
\end{IEEEkeywords}
\section{Introduction}
\section{Introduction} \label{sec:Intro}
\section{Related Work}
\section{Related Work} \label{sec:Rel-Works}
\section{Implementing C/R}
\section{Implementation}
\subsection{Introduction to Intermediate Database Storage}
Chapter 2 and the author of \cite{cook_rage_2018} discuss the challenges of attack graph generation in regards to its scalability. Specifically, the author of \cite{cook_rage_2018} displays results from generations based on 11 assets and 11 exploits that lead to 14,200 total states. Generating an attack or compliance graph based on a large network with a multitude of assets and involving a more thorough exploit or compliance violation checking will prevent the entire graph from being stored in memory as originally designed. This Section discusses the challenges of graph generation in regards to memory, and a solution through the implementation of intermediate database storage.