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Chapter 1 Compliance Graph works

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Noah L. Schrick 2022-03-02 16:23:25 -06:00
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@ -15,7 +15,34 @@ edges. When scaling attack graphs to analyze the modern, interconnected state of
custom vulnerability testing, this becomes infeasible. Similar difficulties arise in related fields, where social networks, bio-informatics, and neural network representations also result in graphs with millions of custom vulnerability testing, this becomes infeasible. Similar difficulties arise in related fields, where social networks, bio-informatics, and neural network representations also result in graphs with millions of
states \cite{zhang_boosting_2017}. Various efforts that will be discussed in Section \ref{sec:related_works} demonstrate methods and techniques that can mitigate these difficulties and improve performance. states \cite{zhang_boosting_2017}. Various efforts that will be discussed in Section \ref{sec:related_works} demonstrate methods and techniques that can mitigate these difficulties and improve performance.
\TUsection{Application to Cybersecurity and Compliance} \TUsection{Application to Compliance}
\TUsubsection{Introduction to Compliance Graphs}
As an alternative to attack graphs for examining vulnerable states and measuring cybersecurity postures, the focus can be narrowed to generate graphs with the purpose of examining compliance or regulation statuses.
These graphs are known as compliance graphs. Compliance graphs can be especially useful for cyber-physical systems, where a greater need for compliance exists. As the authors of \cite{j_hale_compliance_nodate},
\cite{baloyi_guidelines_2019}, and \cite{allman_complying_2006} discuss, cyber-physical systems have seen greater usage, especially in areas like critical infrastructure and Internet of Things. The challenge of
cyber-physical systems lies not only in the demand for cybersecurity of these systems, but also the concern for safe, stable, and undamaged equipment. The industry in which these devices are used can lead to
additional compliance guidelines that must be followed. Compliance graphs are promising tools that can aid in minimizing the difficulties of these systems.
A few alterations are needed to attack graph generators to function as compliance graph generators, and these alterations
are discussed in Section \ref{CG-alter}. Compliance requirements are broad and varying, and can function as safety regulations, maintenance compliance, or any
other regulatory compliance. In the same fashion as attack graphs, compliance graphs are exhaustive, and future system states can be analyzed to determine appropriate steps that need to be taken for
preventative measures \cite{j_hale_compliance_nodate}.
\TUsubsection{Defining Compliance Graphs} \label{CG-alter}
The common features of attack graphs serve separate purposes in compliance graphs. The nodes of an attack graph typically represent the system state that includes the qualities and topologies of all assets
in the network as they pertain to cybersecurity postures. Nodes of a compliance graphs also represent the system state, however they include the qualities and topologies of all assets in the network as they
pertain to compliance regulation. For instance, a quality for a vehicle's maintenance compliance could be described as: \textit{car:months\_since\_oil\_change=6}, or \textit{car:miles\_since\_oil\_change=10,000}.
Edges represent changes to a system state that inserted, modified, or deleted a quality or topology. Using the car example, an edge could represent the addition of more mileage or more time since the last oil change.
One large differentiation of attack graphs and compliance graphs can be seen through topologies. For assets in attack graphs, topologies typically represent a connection of assets through a digital medium. For
compliance graphs, topologies not only need to represent the digital connections of assets, but also need extensions to incorporate hardware devices such as sensors, actuators, or other equipment
\cite{j_hale_compliance_nodate}. In addition, rather than using applicable exploits or vulnerabilities, compliance violation detections should be used. An attack graph generation engine would need to use compliance
parameters rather than exploit files, but would otherwise function similarly in the generation process.
\TUsubsection{Difficulties of Compliance Graphs and Introduction to Thesis Work}
Like attack graphs, compliance graphs suffer from the state space explosion problem. Since compliance graphs are also exhaustive, the resulting networks can grow to incredibly large sizes. Compliance regulations
that need to be checked at each system state such as SOX, HIPAA, GDPR, PCI DSS, or any other regulatory compliance in conjunction with a large number of assets that need to be checked can very quickly produce
these large resulting graphs. The creation of these graphs through a serial approach likewise becomes increasingly infeasible. Due to this, the high-performance computing space presents itself as an appealing
approach. This work aims to extend the attack graph generator engine RAGE presented by the author in \cite{cook_rage_2018} to begin development for compliance graph generation. The example networks in this
work will also be in the compliance graph space, specifically examining vehicle maintenance compliance. This work will also examine approaches to leverage high-performance computing to aid in the generation of
compliance graphs.
\TUsection{Objectives and Contributions} \TUsection{Objectives and Contributions}
The objectives of this thesis are: The objectives of this thesis are:
@ -29,6 +56,7 @@ The objectives of this thesis are:
\end{enumerate} \end{enumerate}
\item{Implement solutions to reduce state space explosion while remaining exhaustive and capturing all necessary information} \item{Implement solutions to reduce state space explosion while remaining exhaustive and capturing all necessary information}
\item{Extend RAGE to function for heterogeneous distributed computing environments} \item{Extend RAGE to function for heterogeneous distributed computing environments}
\item{Utilize RAGE for compliance graph generation}
\end{itemize} \end{itemize}

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@ -6,6 +6,19 @@ Sam Ainsworth and Timothy~M. Jones.
\newblock {\em Proceedings of the International Conference on Supercomputing}, \newblock {\em Proceedings of the International Conference on Supercomputing},
01-03-June, 2016. 01-03-June, 2016.
\bibitem{allman_complying_2006}
Eric Allman.
\newblock Complying with {Compliance}: {Blowing} it off is not an option.
\newblock {\em ACM Queue}, 4(7), 2006.
\bibitem{baloyi_guidelines_2019}
Ntsako Baloyi and Paula Kotzé.
\newblock Guidelines for {Data} {Privacy} {Compliance}: {A} {Focus} on
{Cyberphysical} {Systems} and {Internet} of {Things}.
\newblock In {\em {SAICSIT} '19: {Proceedings} of the {South} {African}
{Institute} of {Computer} {Scientists} and {Information} {Technologists}
2019}, Skukuza South Africa, 2019. Association for Computing Machinery.
\bibitem{berry_graph_2007} \bibitem{berry_graph_2007}
Jonathan Berry and Bruce Hendrickson. Jonathan Berry and Bruce Hendrickson.
\newblock Graph {Analysis} with {High} {Performance} {Computing}. \newblock Graph {Analysis} with {High} {Performance} {Computing}.
@ -23,6 +36,10 @@ Kyle Cook.
payloads.} payloads.}
\newblock National Vulnerability Database, August 2019. \newblock National Vulnerability Database, August 2019.
\bibitem{j_hale_compliance_nodate}
{J. Hale}, P.~Hawrylak, and M.~Papa.
\newblock Compliance {Method} for a {Cyber}-{Physical} {System}.
\bibitem{li_combining_2019} \bibitem{li_combining_2019}
Ming Li, Peter Hawrylak, and John Hale. Ming Li, Peter Hawrylak, and John Hale.
\newblock Combining {OpenCL} and {MPI} to support heterogeneous computing on a \newblock Combining {OpenCL} and {MPI} to support heterogeneous computing on a

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\contentsline {subsubsection}{Worker Nodes}{17}{}% \contentsline {subsection}{\numberline {5.4.1}\it Introduction to the Subgraphing Approach}{19}{}%
\contentsline {subsubsection}{Root Node}{17}{}% \contentsline {subsection}{\numberline {5.4.2}\it Algorithm Design}{19}{}%
\contentsline {subsubsection}{Database Node}{17}{}% \contentsline {subsubsection}{Communication Structure}{19}{}%
\contentsline {subsection}{\numberline {5.4.3}\it Performance Expectations}{17}{}% \contentsline {subsubsection}{Worker Nodes}{19}{}%
\contentsline {chapter}{\numberline {CHAPTER 6: }{\bf \uppercase {PERFORMANCE ANALYSIS}}}{18}{}% \contentsline {subsubsection}{Root Node}{19}{}%
\contentsline {section}{\numberline {6.1}\bf Small Networks}{18}{}% \contentsline {subsubsection}{Database Node}{19}{}%
\contentsline {subsection}{\numberline {6.1.1}\it Test Information}{18}{}% \contentsline {subsection}{\numberline {5.4.3}\it Performance Expectations}{19}{}%
\contentsline {subsection}{\numberline {6.1.2}\it Results}{18}{}% \contentsline {chapter}{\numberline {CHAPTER 6: }{\bf \uppercase {PERFORMANCE ANALYSIS}}}{20}{}%
\contentsline {subsection}{\numberline {6.1.3}\it Analysis}{18}{}% \contentsline {section}{\numberline {6.1}\bf Small Networks}{20}{}%
\contentsline {section}{\numberline {6.2}\bf Large Networks}{18}{}% \contentsline {subsection}{\numberline {6.1.1}\it Test Information}{20}{}%
\contentsline {subsection}{\numberline {6.2.1}\it Test Information}{18}{}% \contentsline {subsection}{\numberline {6.1.2}\it Results}{20}{}%
\contentsline {subsection}{\numberline {6.2.2}\it Results}{18}{}% \contentsline {subsection}{\numberline {6.1.3}\it Analysis}{20}{}%
\contentsline {subsection}{\numberline {6.2.3}\it Analysis}{18}{}% \contentsline {section}{\numberline {6.2}\bf Large Networks}{20}{}%
\contentsline {section}{\numberline {6.3}\bf Large Exploit Lists}{18}{}% \contentsline {subsection}{\numberline {6.2.1}\it Test Information}{20}{}%
\contentsline {subsection}{\numberline {6.3.1}\it Test Information}{18}{}% \contentsline {subsection}{\numberline {6.2.2}\it Results}{20}{}%
\contentsline {subsection}{\numberline {6.3.2}\it Results}{18}{}% \contentsline {subsection}{\numberline {6.2.3}\it Analysis}{20}{}%
\contentsline {subsection}{\numberline {6.3.3}\it Analysis}{18}{}% \contentsline {section}{\numberline {6.3}\bf Large Exploit Lists}{20}{}%
\contentsline {section}{\numberline {6.4}\bf Distributed Hash Tables}{18}{}% \contentsline {subsection}{\numberline {6.3.1}\it Test Information}{20}{}%
\contentsline {subsection}{\numberline {6.4.1}\it Test Information}{19}{}% \contentsline {subsection}{\numberline {6.3.2}\it Results}{20}{}%
\contentsline {subsection}{\numberline {6.4.2}\it Results}{19}{}% \contentsline {subsection}{\numberline {6.3.3}\it Analysis}{20}{}%
\contentsline {subsection}{\numberline {6.4.3}\it Analysis}{19}{}% \contentsline {section}{\numberline {6.4}\bf Distributed Hash Tables}{20}{}%
\contentsline {chapter}{\numberline {CHAPTER 7: }{\bf \uppercase {CONCLUSIONS AND FUTURE WORKS}}}{20}{}% \contentsline {subsection}{\numberline {6.4.1}\it Test Information}{21}{}%
\contentsline {section}{\numberline {7.1}\bf Future Work}{20}{}% \contentsline {subsection}{\numberline {6.4.2}\it Results}{21}{}%
\contentsline {subsection}{\numberline {6.4.3}\it Analysis}{21}{}%
\contentsline {chapter}{\numberline {CHAPTER 7: }{\bf \uppercase {CONCLUSIONS AND FUTURE WORKS}}}{22}{}%
\contentsline {section}{\numberline {7.1}\bf Future Work}{22}{}%
{\hfill \ } {\hfill \ }
\contentsline {section}{\hspace {-\parindent }NOMENCLATURE}{21}{}% \contentsline {section}{\hspace {-\parindent }NOMENCLATURE}{23}{}%
\addvspace {10pt} \addvspace {10pt}
\contentsline {section}{\hspace {-\parindent }BIBLIOGRAPHY}{21}{}% \contentsline {section}{\hspace {-\parindent }BIBLIOGRAPHY}{23}{}%
{\hfill \ } {\hfill \ }
\contentsline {section}{\hspace {-\parindent }APPENDIX A:\ \ {\bf \uppercase {THE FIRST APPENDIX}} }{23}{}% \contentsline {section}{\hspace {-\parindent }APPENDIX A:\ \ {\bf \uppercase {THE FIRST APPENDIX}} }{25}{}%
\contentsline {section}{\hspace {-\parindent }APPENDIX B:\ \ {\bf \uppercase {THE SECOND APPENDIX}} }{24}{}% \contentsline {section}{\hspace {-\parindent }APPENDIX B:\ \ {\bf \uppercase {THE SECOND APPENDIX}} }{26}{}%
\contentsline {section}{\numberline {B.1}\bf A Heading in an Appendix}{24}{}% \contentsline {section}{\numberline {B.1}\bf A Heading in an Appendix}{26}{}%
\contentsline {subsection}{\numberline {B.1.1}\it A Subheading in an Appendix}{24}{}% \contentsline {subsection}{\numberline {B.1.1}\it A Subheading in an Appendix}{26}{}%
\contentsline {subsubsection}{A Sub-subsection in an Appendix}{24}{}% \contentsline {subsubsection}{A Sub-subsection in an Appendix}{26}{}%