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@phdthesis{noauthor_notitle_nodate, @phdthesis{noauthor_notitle_nodate,
} }
@INPROCEEDINGS{9150145,
author={Li, Ming and Hawrylak, Peter J. and Hale, John},
booktitle={2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
title={Implementing an Attack Graph Generator in CUDA},
year={2020},
volume={},
number={},
pages={730-738},
doi={10.1109/IPDPSW50202.2020.00128}}
@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}}

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Chapter1.tex
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@ -56,7 +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} \item{Extend and utilize RAGE for compliance graph generation}
\end{itemize} \end{itemize}

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\TUchapter{RELATED WORKS} \TUchapter{RELATED WORKS}
Many authors and researchers have developed or extended attack graphs since their beginning Many authors and researchers have developed or extended attack graphs since their beginning
as attack trees. This Chapter reviews a few of their efforts as they relate to this work. as attack trees. This Chapter reviews a few of their efforts as they relate to this work and to graph generation.
\TUsection{Introduction to Graph Generation} \TUsection{Introduction to Graph Generation}
Graph generation as a broad topic has many challenges that prevent full actualization of computation seen from a theoretical standpoint.
In actuality, graph generation often achieves only a very low percentage of its expected performance \cite{berry_graph_2007}. A few reasons
for this occurence lies in the underlying mechanisms of graph generation. The generation is predominantly memory based (as opposed to based on processor speed),
where performance is tied to memory access time, the complexity of data dependency, and coarseness of parallelism \cite{berry_graph_2007}, \cite{zhang_boosting_2017},
\cite{ainsworth_graph_2016}. The graph generation process is typically quite poor, resulting in lower performance results. Graphs consume large amounts of memory through their
nodes and edges, graph data structures suffer from poor cache locality, and memory latency from the processor-memory gap all slow the generation process dramatically
\cite{berry_graph_2007}, \cite{ainsworth_graph_2016}. Section \ref{sec:gen_improv} discusses a few works that can be used to improve the graph generation process, and Section
\ref{sec:related_works} discusses a few works specific to attack graph generation improvements.
\TUsection{Improvements to Attack Graph Generation} \label{sec:related_works} \TUsection{Graph Generation Improvements} \label{sec:gen_improv}
For architectural and hardware techinques for generation improvement, the authors of \cite{ainsworth_graph_2016} discuss the high cache miss rate, and how general prefetching
leads does not increase the prediction rate due to non-sequenial graph structures and data-dependent access patterns. However, the authors continue to discuss that the generation
algorithm is known in advance, so explicit tuning of the hardware prefetcher to follow the traversal order pattern can lead to better performance. The authors were able to achieve
over 2x performance improvement of a breadth-first search approach with this method. Another hardware approach is to make use of accelerators. The authors of \cite{yao_efficient_2018}
present an approach for minimizing the slowdown caused by the underlying graph atomic functions. By using the atomic function patterns, the authors utilized pipeline stages where vertex
updates can be processed in parallel dynamically. Other works, such as those by the authors of \cite{zhang_boosting_2017} and \cite{dai_fpgp_2016}, leverage field-programmable gate arrays
(FPGAs) for graph generation in the HPC space through various means. This includes reducing memory strain, storing repeatedly accessed lists, storing results, or other storage through the
on-chip block RAM, or even levering Hybrid Memory Cubes for optimizing parallel access.
\TUsection{Attack Dependency Graphs} From a data structure standpoint, the authors of \cite{arifuzzaman_fast_2015} describe the infeasibility of adjacency matrices in large-scale graphs, and this work and other works such as those
by the authors of \cite{yu_construction_2018} and \cite{liakos_memory-optimized_2016} discuss the appeal of distibuting a graph representation among systems. The author of
\cite{liakos_memory-optimized_2016} disuss the usage of distributed adjacency lists for assinging vertices to workers. The authors of \cite{liakos_memory-optimized_2016} and
\cite{balaji_graph_2016} present other techniques for minimizing communication costs by achieving high compression ratios while maintaining a low compression cost. The Boost Graph Library
and the Parallel Boost Graph Library both provide appealing features for working with graps, with the latter library notably having interoperability with MPI, Graphviz, and METIS
\cite{noauthor_overview_nodate}, \cite {noauthor_boost_nodate}.
\TUsection{Compliance Graphs} \TUsection{Improvements Specific to Attack Graph Generation} \label{sec:related_works}
As a means of improving scalability of attack graphs, the authors of \cite{ou_scalable_2006} present a new representation scheme. Traditional attack graphs encode the entire network at each state,
but this representation 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} represent 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 demonstrate an improvement in scalability through refining the desirable information.
Another approach for generation improvement is through parallelization. The authors of \cite{li_concurrency_2019} leverage OpenMP to parallelize the exploration of a FIFO queue. This parallelization also
includes the utilization of OpenMP's dynamic scheduling. In this approach, each thread receives a state to explore, where a critical section is employed to handle the atomic functions of merging new state
information while avoiding collisions, race conditions, or stale data usage. The authors measured a 10x speedup over the serial algorithm. The authors of \cite{9150145} present a parallel generation
approach using CUDA, where speedup is obtained through a large number of CUDA cores. For a distributed approach, the authors of \cite{7087377} present a technique for utilizing reachability hyper-graph partitioning
and a virtual shared memory abstraction to prevent duplicate work by multiple nodes. This work had promising results in terms of limiting the state-space explosion and speedup as the number of network hosts increases.

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@ -1,5 +1,11 @@
\begin{thebibliography}{10} \begin{thebibliography}{10}
\bibitem{noauthor_boost_nodate}
The {Boost} {Graph} {Library} - 1.75.0.
\bibitem{noauthor_overview_nodate}
An {Overview} of the {Parallel} {Boost} {Graph} {Library} - 1.75.0.
\bibitem{ainsworth_graph_2016} \bibitem{ainsworth_graph_2016}
Sam Ainsworth and Timothy~M. Jones. Sam Ainsworth and Timothy~M. Jones.
\newblock Graph prefetching using data structure knowledge. \newblock Graph prefetching using data structure knowledge.
@ -11,6 +17,19 @@ Eric Allman.
\newblock Complying with {Compliance}: {Blowing} it off is not an option. \newblock Complying with {Compliance}: {Blowing} it off is not an option.
\newblock {\em ACM Queue}, 4(7), 2006. \newblock {\em ACM Queue}, 4(7), 2006.
\bibitem{arifuzzaman_fast_2015}
Shaikh Arifuzzaman and Maleq Khan.
\newblock Fast parallel conversion of edge list to adjacency list for
large-scale graphs.
\newblock In {\em {HPC} '15: {Proceedings} of the {Symposium} on {High}
{Performance} {Computing}}, pages 17--24, April 2015.
\bibitem{balaji_graph_2016}
Janani Balaji and Rajshekhar Sunderraman.
\newblock Graph {Topology} {Abstraction} for {Distributed} {Path} {Queries}.
\newblock In {\em {HPGP} '16: {Proceedings} of the {ACM} {Workshop} on {High}
{Performance} {Graph} {Processing}}, pages 27--34, May 2016.
\bibitem{baloyi_guidelines_2019} \bibitem{baloyi_guidelines_2019}
Ntsako Baloyi and Paula Kotzé. Ntsako Baloyi and Paula Kotzé.
\newblock Guidelines for {Data} {Privacy} {Compliance}: {A} {Focus} on \newblock Guidelines for {Data} {Privacy} {Compliance}: {A} {Focus} on
@ -29,6 +48,12 @@ Kyle Cook.
\newblock {\em {RAGE}: {The} {Rage} {Attack} {Graph} {Engine}}. \newblock {\em {RAGE}: {The} {Rage} {Attack} {Graph} {Engine}}.
\newblock PhD thesis, 2018. \newblock PhD thesis, 2018.
\bibitem{cook_scalable_2016}
Kyle Cook, Thomas Shaw, John Hale, and Peter Hawrylak.
\newblock Scalable attack graph generation.
\newblock {\em Proceedings of the 11th Annual Cyber and Information Security
Research Conference, CISRC 2016}, 2016.
\bibitem{CVE-2019-10747} \bibitem{CVE-2019-10747}
{set-value is vulnerable to Prototype Pollution in versions lower than 3.0.1. {set-value is vulnerable to Prototype Pollution in versions lower than 3.0.1.
The function mixin-deep could be tricked into adding or modifying properties The function mixin-deep could be tricked into adding or modifying properties
@ -36,10 +61,23 @@ Kyle Cook.
payloads.} payloads.}
\newblock National Vulnerability Database, August 2019. \newblock National Vulnerability Database, August 2019.
\bibitem{dai_fpgp_2016}
Guohao Dai, Yuze Chi, Yu~Wang, and Huazhong Yang.
\newblock {FPGP}: {Graph} processing framework on {FPGA}: {A} case study of
breadth-first search.
\newblock {\em FPGA 2016 - Proceedings of the 2016 ACM/SIGDA International
Symposium on Field-Programmable Gate Arrays}, pages 105--110, 2016.
\bibitem{j_hale_compliance_nodate} \bibitem{j_hale_compliance_nodate}
{J. Hale}, P.~Hawrylak, and M.~Papa. {J. Hale}, P.~Hawrylak, and M.~Papa.
\newblock Compliance {Method} for a {Cyber}-{Physical} {System}. \newblock Compliance {Method} for a {Cyber}-{Physical} {System}.
\bibitem{7087377}
Kerem Kaynar and Fikret Sivrikaya.
\newblock Distributed attack graph generation.
\newblock {\em IEEE Transactions on Dependable and Secure Computing},
13(5):519--532, 2016.
\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
@ -52,6 +90,20 @@ Ming Li, Peter Hawrylak, and John Hale.
\newblock {\em Proceedings - 2019 2nd International Conference on Data \newblock {\em Proceedings - 2019 2nd International Conference on Data
Intelligence and Security, ICDIS 2019}, pages 174--179, 2019. Intelligence and Security, ICDIS 2019}, pages 174--179, 2019.
\bibitem{9150145}
Ming Li, Peter~J. Hawrylak, and John Hale.
\newblock Implementing an attack graph generator in cuda.
\newblock In {\em 2020 IEEE International Parallel and Distributed Processing
Symposium Workshops (IPDPSW)}, pages 730--738, 2020.
\bibitem{liakos_memory-optimized_2016}
Panagiotis Liakos, Katia Papakonstantinopoulou, and Alex Delis.
\newblock Memory-{Optimized} {Distributed} {Graph} {Processing} through {Novel}
{Compression} {Techniques}.
\newblock In {\em {CIKM} '16: {Proceedings} of the 25th {ACM} {International}
{Conference} on {Information} and {Knowledge} {Management}}, pages
2317--2322, October 2016.
\bibitem{ou_scalable_2006} \bibitem{ou_scalable_2006}
Xinming Ou, Wayne~F Boyer, and Miles~A Mcqueen. Xinming Ou, Wayne~F Boyer, and Miles~A Mcqueen.
\newblock A {Scalable} {Approach} to {Attack} {Graph} {Generation}. \newblock A {Scalable} {Approach} to {Attack} {Graph} {Generation}.
@ -74,6 +126,22 @@ O.~Sheyner, J.~Haines, S.~Jha, R.. Lippmann, and J.~Wing.
\newblock {\em Proceeding of 2002 IEEE Symposium on Security and Privacy}, \newblock {\em Proceeding of 2002 IEEE Symposium on Security and Privacy},
pages 254--265, 2002. pages 254--265, 2002.
\bibitem{yao_efficient_2018}
Pengcheng Yao, Long Zheng, Xiaofei Liao, Hai Jin, and Bingsheng He.
\newblock An efficient graph accelerator with parallel data conflict
management.
\newblock {\em Parallel Architectures and Compilation Techniques - Conference
Proceedings, PACT}, 2018.
\bibitem{yu_construction_2018}
Xinjie Yu, Wentao Chen, Jiajia Miao, Jian Chen, Handong Mao, Qiong Luo, and Lin
Gu.
\newblock The {Construction} of {Large} {Graph} {Data} {Structures} in a
{Scalable} {Distributed} {Message} {System}.
\newblock In {\em {HPCCT} 2018: {Proceedings} of the 2018 2nd {High}
{Performance} {Computing} and {Cluster} {Technologies} {Conference}}, pages
6--10, June 2018.
\bibitem{zhang_boosting_2017} \bibitem{zhang_boosting_2017}
Jialiang Zhang, Soroosh Khoram, and Jing Li. Jialiang Zhang, Soroosh Khoram, and Jing Li.
\newblock Boosting the performance of {FPGA}-based graph processor using hybrid \newblock Boosting the performance of {FPGA}-based graph processor using hybrid

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{\vspace {\baselineskip }} {\vspace {\baselineskip }}
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Schrick-Noah_MS-Thesis.toc
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\contentsline {section}{\numberline {1.3}\bf Objectives and Contributions}{4}{}% \contentsline {section}{\numberline {1.3}\bf Objectives and Contributions}{4}{}%
\contentsline {chapter}{\numberline {CHAPTER 2: }{\bf \uppercase {RELATED WORKS}}}{5}{}% \contentsline {chapter}{\numberline {CHAPTER 2: }{\bf \uppercase {RELATED WORKS}}}{5}{}%
\contentsline {section}{\numberline {2.1}\bf Introduction to Graph Generation}{5}{}% \contentsline {section}{\numberline {2.1}\bf Introduction to Graph Generation}{5}{}%
\contentsline {section}{\numberline {2.2}\bf Improvements to Attack Graph Generation}{5}{}% \contentsline {section}{\numberline {2.2}\bf Graph Generation Improvements}{5}{}%
\contentsline {section}{\numberline {2.3}\bf Attack Dependency Graphs}{5}{}% \contentsline {section}{\numberline {2.3}\bf Improvements Specific to Attack Graph Generation}{6}{}%
\contentsline {section}{\numberline {2.4}\bf Compliance Graphs}{5}{}% \contentsline {chapter}{\numberline {CHAPTER 3: }{\bf \uppercase {UTILITY EXTENSIONS TO THE RAGE ATTACK GRAPH GENERATOR}}}{8}{}%
\contentsline {chapter}{\numberline {CHAPTER 3: }{\bf \uppercase {UTILITY EXTENSIONS TO THE RAGE ATTACK GRAPH GENERATOR}}}{6}{}% \contentsline {section}{\numberline {3.1}\bf Path Walking}{8}{}%
\contentsline {section}{\numberline {3.1}\bf Path Walking}{6}{}% \contentsline {section}{\numberline {3.2}\bf Compound Operators}{8}{}%
\contentsline {section}{\numberline {3.2}\bf Compound Operators}{6}{}% \contentsline {section}{\numberline {3.3}\bf Color Coding}{10}{}%
\contentsline {section}{\numberline {3.3}\bf Color Coding}{8}{}% \contentsline {section}{\numberline {3.4}\bf Intermediate Database Storage}{11}{}%
\contentsline {section}{\numberline {3.4}\bf Intermediate Database Storage}{9}{}% \contentsline {subsection}{\numberline {3.4.1}\it Memory Constraint Difficulties}{12}{}%
\contentsline {subsection}{\numberline {3.4.1}\it Memory Constraint Difficulties}{10}{}% \contentsline {subsection}{\numberline {3.4.2}\it Maximizing Performance with Intermediate Database Storage}{13}{}%
\contentsline {subsection}{\numberline {3.4.2}\it Maximizing Performance with Intermediate Database Storage}{11}{}% \contentsline {subsection}{\numberline {3.4.3}\it Portability}{14}{}%
\contentsline {subsection}{\numberline {3.4.3}\it Portability}{12}{}% \contentsline {section}{\numberline {3.5}\bf Relational Operators}{15}{}%
\contentsline {section}{\numberline {3.5}\bf Relational Operators}{13}{}% \contentsline {chapter}{\numberline {CHAPTER 4: }{\bf \uppercase {SYNCHRONOUS FIRING}}}{16}{}%
\contentsline {chapter}{\numberline {CHAPTER 4: }{\bf \uppercase {SYNCHRONOUS FIRING}}}{14}{}% \contentsline {section}{\numberline {4.1}\bf Introduction}{16}{}%
\contentsline {section}{\numberline {4.1}\bf Introduction}{14}{}% \contentsline {subsection}{\numberline {4.1.1}\it Synchronous Firing in Literature}{16}{}%
\contentsline {subsection}{\numberline {4.1.1}\it Synchronous Firing in Literature}{14}{}% \contentsline {section}{\numberline {4.2}\bf Necessary Components}{16}{}%
\contentsline {section}{\numberline {4.2}\bf Necessary Components}{14}{}% \contentsline {section}{\numberline {4.3}\bf Example Networks and Results}{16}{}%
\contentsline {section}{\numberline {4.3}\bf Example Networks and Results}{14}{}% \contentsline {subsection}{\numberline {4.3.1}\it Example Networks}{16}{}%
\contentsline {subsection}{\numberline {4.3.1}\it Example Networks}{14}{}% \contentsline {subsection}{\numberline {4.3.2}\it Results}{16}{}%
\contentsline {subsection}{\numberline {4.3.2}\it Results}{14}{}% \contentsline {chapter}{\numberline {CHAPTER 5: }{\bf \uppercase {Utilization OF MESSAGE PASSING INTERFACE}}}{17}{}%
\contentsline {chapter}{\numberline {CHAPTER 5: }{\bf \uppercase {Utilization OF MESSAGE PASSING INTERFACE}}}{15}{}% \contentsline {section}{\numberline {5.1}\bf Introduction to MPI Utilization for Attack Graph Generation}{17}{}%
\contentsline {section}{\numberline {5.1}\bf Introduction to MPI Utilization for Attack Graph Generation}{15}{}% \contentsline {section}{\numberline {5.2}\bf Necessary Components}{17}{}%
\contentsline {section}{\numberline {5.2}\bf Necessary Components}{15}{}% \contentsline {subsection}{\numberline {5.2.1}\it Serialization}{17}{}%
\contentsline {subsection}{\numberline {5.2.1}\it Serialization}{15}{}% \contentsline {subsection}{\numberline {5.2.2}\it Data Consistency}{18}{}%
\contentsline {subsection}{\numberline {5.2.2}\it Data Consistency}{16}{}% \contentsline {section}{\numberline {5.3}\bf Tasking Approach}{18}{}%
\contentsline {section}{\numberline {5.3}\bf Tasking Approach}{16}{}% \contentsline {subsection}{\numberline {5.3.1}\it Introduction to the Tasking Approach}{18}{}%
\contentsline {subsection}{\numberline {5.3.1}\it Introduction to the Tasking Approach}{16}{}% \contentsline {subsection}{\numberline {5.3.2}\it Algorithm Design}{18}{}%
\contentsline {subsection}{\numberline {5.3.2}\it Algorithm Design}{16}{}% \contentsline {subsubsection}{Communication Structure}{18}{}%
\contentsline {subsubsection}{Communication Structure}{16}{}% \contentsline {subsubsection}{Task Zero}{18}{}%
\contentsline {subsubsection}{Task Zero}{16}{}% \contentsline {subsubsection}{Task One}{18}{}%
\contentsline {subsubsection}{Task One}{16}{}% \contentsline {subsubsection}{Task Two}{18}{}%
\contentsline {subsubsection}{Task Two}{16}{}% \contentsline {subsubsection}{Task Three}{18}{}%
\contentsline {subsubsection}{Task Three}{16}{}% \contentsline {subsubsection}{Task Four}{21}{}%
\contentsline {subsubsection}{Task Four}{19}{}% \contentsline {subsubsection}{Task Five}{21}{}%
\contentsline {subsubsection}{Task Five}{19}{}% \contentsline {subsection}{\numberline {5.3.3}\it Performance Expectations}{21}{}%
\contentsline {subsection}{\numberline {5.3.3}\it Performance Expectations}{19}{}% \contentsline {section}{\numberline {5.4}\bf Subgraphing Approach}{21}{}%
\contentsline {section}{\numberline {5.4}\bf Subgraphing Approach}{19}{}% \contentsline {subsection}{\numberline {5.4.1}\it Introduction to the Subgraphing Approach}{21}{}%
\contentsline {subsection}{\numberline {5.4.1}\it Introduction to the Subgraphing Approach}{19}{}% \contentsline {subsection}{\numberline {5.4.2}\it Algorithm Design}{21}{}%
\contentsline {subsection}{\numberline {5.4.2}\it Algorithm Design}{19}{}% \contentsline {subsubsection}{Communication Structure}{21}{}%
\contentsline {subsubsection}{Communication Structure}{19}{}% \contentsline {subsubsection}{Worker Nodes}{21}{}%
\contentsline {subsubsection}{Worker Nodes}{19}{}% \contentsline {subsubsection}{Root Node}{21}{}%
\contentsline {subsubsection}{Root Node}{19}{}% \contentsline {subsubsection}{Database Node}{21}{}%
\contentsline {subsubsection}{Database Node}{19}{}% \contentsline {subsection}{\numberline {5.4.3}\it Performance Expectations}{21}{}%
\contentsline {subsection}{\numberline {5.4.3}\it Performance Expectations}{19}{}% \contentsline {chapter}{\numberline {CHAPTER 6: }{\bf \uppercase {PERFORMANCE ANALYSIS}}}{22}{}%
\contentsline {chapter}{\numberline {CHAPTER 6: }{\bf \uppercase {PERFORMANCE ANALYSIS}}}{20}{}% \contentsline {section}{\numberline {6.1}\bf Small Networks}{22}{}%
\contentsline {section}{\numberline {6.1}\bf Small Networks}{20}{}% \contentsline {subsection}{\numberline {6.1.1}\it Test Information}{22}{}%
\contentsline {subsection}{\numberline {6.1.1}\it Test Information}{20}{}% \contentsline {subsection}{\numberline {6.1.2}\it Results}{22}{}%
\contentsline {subsection}{\numberline {6.1.2}\it Results}{20}{}% \contentsline {subsection}{\numberline {6.1.3}\it Analysis}{22}{}%
\contentsline {subsection}{\numberline {6.1.3}\it Analysis}{20}{}% \contentsline {section}{\numberline {6.2}\bf Large Networks}{22}{}%
\contentsline {section}{\numberline {6.2}\bf Large Networks}{20}{}% \contentsline {subsection}{\numberline {6.2.1}\it Test Information}{22}{}%
\contentsline {subsection}{\numberline {6.2.1}\it Test Information}{20}{}% \contentsline {subsection}{\numberline {6.2.2}\it Results}{22}{}%
\contentsline {subsection}{\numberline {6.2.2}\it Results}{20}{}% \contentsline {subsection}{\numberline {6.2.3}\it Analysis}{22}{}%
\contentsline {subsection}{\numberline {6.2.3}\it Analysis}{20}{}% \contentsline {section}{\numberline {6.3}\bf Large Exploit Lists}{22}{}%
\contentsline {section}{\numberline {6.3}\bf Large Exploit Lists}{20}{}% \contentsline {subsection}{\numberline {6.3.1}\it Test Information}{22}{}%
\contentsline {subsection}{\numberline {6.3.1}\it Test Information}{20}{}% \contentsline {subsection}{\numberline {6.3.2}\it Results}{22}{}%
\contentsline {subsection}{\numberline {6.3.2}\it Results}{20}{}% \contentsline {subsection}{\numberline {6.3.3}\it Analysis}{22}{}%
\contentsline {subsection}{\numberline {6.3.3}\it Analysis}{20}{}% \contentsline {section}{\numberline {6.4}\bf Distributed Hash Tables}{22}{}%
\contentsline {section}{\numberline {6.4}\bf Distributed Hash Tables}{20}{}% \contentsline {subsection}{\numberline {6.4.1}\it Test Information}{23}{}%
\contentsline {subsection}{\numberline {6.4.1}\it Test Information}{21}{}% \contentsline {subsection}{\numberline {6.4.2}\it Results}{23}{}%
\contentsline {subsection}{\numberline {6.4.2}\it Results}{21}{}% \contentsline {subsection}{\numberline {6.4.3}\it Analysis}{23}{}%
\contentsline {subsection}{\numberline {6.4.3}\it Analysis}{21}{}% \contentsline {chapter}{\numberline {CHAPTER 7: }{\bf \uppercase {CONCLUSIONS AND FUTURE WORKS}}}{24}{}%
\contentsline {chapter}{\numberline {CHAPTER 7: }{\bf \uppercase {CONCLUSIONS AND FUTURE WORKS}}}{22}{}% \contentsline {section}{\numberline {7.1}\bf Future Work}{24}{}%
\contentsline {section}{\numberline {7.1}\bf Future Work}{22}{}%
{\hfill \ } {\hfill \ }
\contentsline {section}{\hspace {-\parindent }NOMENCLATURE}{23}{}% \contentsline {section}{\hspace {-\parindent }NOMENCLATURE}{25}{}%
\addvspace {10pt} \addvspace {10pt}
\contentsline {section}{\hspace {-\parindent }BIBLIOGRAPHY}{23}{}% \contentsline {section}{\hspace {-\parindent }BIBLIOGRAPHY}{25}{}%
{\hfill \ } {\hfill \ }
\contentsline {section}{\hspace {-\parindent }APPENDIX A:\ \ {\bf \uppercase {THE FIRST APPENDIX}} }{25}{}% \contentsline {section}{\hspace {-\parindent }APPENDIX A:\ \ {\bf \uppercase {THE FIRST APPENDIX}} }{28}{}%
\contentsline {section}{\hspace {-\parindent }APPENDIX B:\ \ {\bf \uppercase {THE SECOND APPENDIX}} }{26}{}% \contentsline {section}{\hspace {-\parindent }APPENDIX B:\ \ {\bf \uppercase {THE SECOND APPENDIX}} }{29}{}%
\contentsline {section}{\numberline {B.1}\bf A Heading in an Appendix}{26}{}% \contentsline {section}{\numberline {B.1}\bf A Heading in an Appendix}{29}{}%
\contentsline {subsection}{\numberline {B.1.1}\it A Subheading in an Appendix}{26}{}% \contentsline {subsection}{\numberline {B.1.1}\it A Subheading in an Appendix}{29}{}%
\contentsline {subsubsection}{A Sub-subsection in an Appendix}{26}{}% \contentsline {subsubsection}{A Sub-subsection in an Appendix}{29}{}%