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59
Bibliography.bib
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@ -14,6 +14,63 @@
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@ARTICLE{7087377,
author={Kaynar, Kerem and Sivrikaya, Fikret},
journal={IEEE Transactions on Dependable and Secure Computing},
title={Distributed Attack Graph Generation},
year={2016},
volume={13},
number={5},
pages={519-532},
doi={10.1109/TDSC.2015.2423682}
}
@INPROCEEDINGS{LAPA,
author={Yi, Feng and Cai, Huang Yi and Xin, Fu Zheng},
booktitle={2018 IEEE International Conference on Networking, Architecture and Storage (NAS)},
title={A Logic-Based Attack Graph for Analyzing Network Security Risk Against Potential Attack},
year={2018},
volume={},
number={},
pages={1-4},
doi={10.1109/NAS.2018.8515733}
}
@INPROCEEDINGS{AG-Sample,
author={Subasi, Omer and Purohit, Sumit and Bhattacharya, Arnab and Chatterjee, Samrat},
booktitle={2022 IEEE International Symposium on Technologies for Homeland Security (HST)},
title={Impact-Driven Sampling Strategies for Hybrid Attack Graphs},
year={2022},
volume={},
number={},
pages={1-7},
doi={10.1109/HST56032.2022.10025439}
}
@INPROCEEDINGS{GraphDB,
author={Simon-Nagy, Gabriella and Fleiner, Rita and Bánáti, Anna},
booktitle={2022 IEEE 20th Jubilee International Symposium on Intelligent Systems and Informatics (SISY)},
title={Attack Graph Implementation in Graph Database},
year={2022},
volume={},
number={},
pages={000127-000132},
doi={10.1109/SISY56759.2022.10036309}
}
@INPROCEEDINGS{Graph-DB,
author={Yuan, Bintao and Pan, Zulie and Shi, Fan and Li, Zhenhan},
booktitle={2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)},
title={An Attack Path Generation Methods Based on Graph Database},
year={2020},
volume={1},
number={},
pages={1905-1910},
doi={10.1109/ITNEC48623.2020.9085039}
}
@book{hursey2010coordinated,
title={Coordinated checkpoint/restart process fault tolerance for MPI applications on HPC systems},
author={Hursey, Joshua},
@ -77,7 +134,7 @@
file = {Graph Analysis With High-Performance Computing:/home/noah/Zotero/storage/T84DCNCC/Graph Analysis With High-Performance Computing.pdf:application/pdf},
}
@phdthesis{cook_rage_2018,
@mastersthesis{cook_rage_2018,
title = {{RAGE}: {The} {Rage} {Attack} {Graph} {Engine}},
author = {Cook, Kyle},
school = {The {University} of {Tulsa}},

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Schrick-Noah_AG-CG-CR.aux
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@ -37,9 +37,14 @@
\newlabel{sec:Intro}{{I}{1}{Introduction}{section.1}{}}
\@writefile{toc}{\contentsline {section}{\numberline {II}Related Work}{1}{section.2}\protected@file@percent }
\newlabel{sec:Rel-Works}{{II}{1}{Related Work}{section.2}{}}
\citation{GraphDB}
\citation{Graph-DB}
\citation{ou_scalable_2006}
\citation{LAPA}
\citation{cook_scalable_2016}
\citation{li_concurrency_2019}
\citation{AG-Sample}
\citation{7087377}
\citation{cook_rage_2018}
\citation{li_concurrency_2019}
\citation{li_combining_2019}
@ -57,7 +62,6 @@
\citation{CR-Simple}
\bibdata{Bibliography}
\bibcite{schneier_modeling_1999}{1}
\bibcite{j_hale_compliance_nodate}{2}
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@ -65,7 +69,7 @@
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\bibcite{j_hale_compliance_nodate}{2}
\bibcite{cook_rage_2018}{3}
\bibcite{berry_graph_2007}{4}
\bibcite{zhang_boosting_2017}{5}
@ -76,9 +80,15 @@
\bibcite{SCR}{10}
\bibcite{dmtcp}{11}
\bibcite{BLCR}{12}
\bibcite{cook_scalable_2016}{13}
\bibcite{li_concurrency_2019}{14}
\bibcite{li_combining_2019}{15}
\bibcite{CR-Simple}{16}
\bibcite{GraphDB}{13}
\bibcite{Graph-DB}{14}
\bibcite{LAPA}{15}
\bibcite{cook_scalable_2016}{16}
\bibcite{li_concurrency_2019}{17}
\bibcite{AG-Sample}{18}
\bibcite{7087377}{19}
\bibcite{li_combining_2019}{20}
\bibcite{CR-Simple}{21}
\bibstyle{ieeetr}
\@writefile{toc}{\contentsline {section}{References}{5}{section*.1}\protected@file@percent }
\gdef \@abspage@last{5}

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Schrick-Noah_AG-CG-CR.bbl
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@ -10,8 +10,8 @@ B.~Schneier, ``Modeling {Security} {Threats},'' {\em Dr. Dobb's Journal}, 1999.
\newblock U.S. Patent Number 9,471,789, Oct. 18, 2016.
\bibitem{cook_rage_2018}
K.~Cook, {\em {RAGE}: {The} {Rage} {Attack} {Graph} {Engine}}.
\newblock PhD thesis, The {University} of {Tulsa}, 2018.
K.~Cook, ``{RAGE}: {The} {Rage} {Attack} {Graph} {Engine},'' Master's thesis,
The {University} of {Tulsa}, 2018.
\bibitem{berry_graph_2007}
J.~Berry and B.~Hendrickson, ``Graph {Analysis} with {High} {Performance}
@ -58,6 +58,23 @@ J.~Ansel, K.~Arya, and G.~Cooperman, ``Dmtcp: Transparent checkpointing for
J.~Duell, P.~H. Hargrove, and E.~S. Roman, ``Requirements for linux
checkpoint/restart,'' 2 2002.
\bibitem{GraphDB}
G.~Simon-Nagy, R.~Fleiner, and A.~Bánáti, ``Attack graph implementation in
graph database,'' in {\em 2022 IEEE 20th Jubilee International Symposium on
Intelligent Systems and Informatics (SISY)}, pp.~000127--000132, 2022.
\bibitem{Graph-DB}
B.~Yuan, Z.~Pan, F.~Shi, and Z.~Li, ``An attack path generation methods based
on graph database,'' in {\em 2020 IEEE 4th Information Technology,
Networking, Electronic and Automation Control Conference (ITNEC)}, vol.~1,
pp.~1905--1910, 2020.
\bibitem{LAPA}
F.~Yi, H.~Y. Cai, and F.~Z. Xin, ``A logic-based attack graph for analyzing
network security risk against potential attack,'' in {\em 2018 IEEE
International Conference on Networking, Architecture and Storage (NAS)},
pp.~1--4, 2018.
\bibitem{cook_scalable_2016}
K.~Cook, T.~Shaw, J.~Hale, and P.~Hawrylak, ``Scalable attack graph
generation,'' {\em Proceedings of the 11th Annual Cyber and Information
@ -68,6 +85,17 @@ 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{AG-Sample}
O.~Subasi, S.~Purohit, A.~Bhattacharya, and S.~Chatterjee, ``Impact-driven
sampling strategies for hybrid attack graphs,'' in {\em 2022 IEEE
International Symposium on Technologies for Homeland Security (HST)},
pp.~1--7, 2022.
\bibitem{7087377}
K.~Kaynar and F.~Sivrikaya, ``Distributed attack graph generation,'' {\em IEEE
Transactions on Dependable and Secure Computing}, vol.~13, no.~5,
pp.~519--532, 2016.
\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

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@ -67,11 +67,7 @@ Due to the runtime requirements and scalability challenges imposed by graph gene
\section{Related Work} \label{sec:Rel-Works}
Numerous efforts have been presented for C/R techniques with various categories available. The authors of \cite{CR-Survey} and \cite{hursey2010coordinated} discuss three categories of C/R, which include application-level, user-level, and system-level. Each approach draws upon advantages that appeal toward different aspects of reliability. User-level checkpointing, though has greater simplicity, results in larger checkpoints. System-level requires compatibility with the operating system and any libraries used for the application. Application-level checkpointing requires additional work for the implementation, but resuls in smaller, faster C/R. The authors of \cite{SCR} present the SCR (Scalable Checkpoint/Restart) library, which has seen widespread adoption due to its minimal overhead. DMTCP (Distributed MultiThreaded Checkpointing) \cite{dmtcp} and BLCR (Berkely Lab Checkpoint/Restart) \cite{BLCR} are two other commonly-used C/R approaches.
Other investigations into attack and compliance graphs attempt to improve performance and scalability to mitigate state space explosion or lengthy runtimes, rather than focus on C/R. As a means of improving scalability of attack graphs themselves, the authors of \cite{ou_scalable_2006} present a new representation scheme. Traditional attack graphs encode the entire network at each state,
but the representation presented by the authors uses logical statements to represent a portion of the network at each node. This is called a logical attack graph. This approach led to the reduction of the generation process
to quadratic time and reduced the number of nodes in the resulting graph to $\mathcal{O}({n}^2)$. However, this approach does require more analysis for identifying attack vectors. Another approach
presented by the authors of \cite{cook_scalable_2016} represents a description of systems and their qualities and topologies as a state, with a queue of unexplored states. This work was continued by the
authors of \cite{li_concurrency_2019} by implementing a hash table among other features. Each of these works demonstrates an improvement in scalability through refining the desirable information output.
Other investigations into attack and compliance graphs attempt to improve performance and scalability to mitigate state space explosion or lengthy runtimes, rather than focus on C/R. These investigations include the works by the authors of \cite{GraphDB}, which implement attack graph methodologies using Neo4j for efficient storage techniques. This approach has seen other implementations, such as that shown by the authors of \cite{Graph-DB}. Other attack graph scalability studies involve the alteration of the representation schemes. The authors of \cite{ou_scalable_2006} make use of logical statements for logical attack graphs. This approach has seen continued investigations, and similar logic-based attack graphs can be seen in the work presented by the authors of \cite{LAPA}. These logical based attack graphs aim to improve scalability by minimizing the resulting information. Other representation schemes include those seen by the authors of \cite{cook_scalable_2016} and the authors of \cite{li_concurrency_2019}, which make use of qualities and topologies through graph states. Scalability improvements have also been examined through sampling, such as the approach presented by the authors of \cite{AG-Sample}. Parallelization techniques have been investigated for runtime improvement, and successful results have been seen in the work by the authors of \cite{7087377}.
\section{Methodology}
\subsection{Checkpointing}