Edits to Abstract

Chapter1.tex

@@ -24,7 +24,7 @@ cyber-physical systems lies not only in the demand for cybersecurity of these sy
 additional compliance guidelines that must be followed, increasing the complexity required for examining compliance statuses. Compliance graphs are promising tools that can aid in minimizing the overhead caused by these systems and the regulations they must follow.
 
 A few alterations are required for 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 
+are discussed in Section \ref{sec: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{sec:CG-alter}

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@@ -39,7 +39,7 @@
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-\setcounter{subsection}{0}
+\setcounter{subsection}{3}
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Chapter3.tex

@@ -20,6 +20,21 @@ of the original attack graph.
         \label{fig:PW}
 \end{figure}
 
+\TUsection{Color Coding}
+As a visual aid for analysis purposes, color coding was another feature implemented as a postprocessing tool for RAGE. When viewing the output graph of RAGE, all states are
+visibly identical in appearance apart from number of edges, edge IDs, and state IDs. To allow for visual differentiation, color coding can be enabled in the run script.
+Color coding currently functions by working through the graph output text file, but it can be extended to read directly from the PostgreSQL database instead. The feature scans through the
+output file, and locates states that have $``compliance$\_$vios > 0"$ or $``compliance$\_$vio = true"$. For states that meet these
+properties, the color coding feature will add a color to the Graphviz DOT file through the $[color=COL]$ attribute for the given node, where \textit{COL} is assigned based on severity.
+For this version of color coding, severity is determined by the total number of compliance violations a node has, but future versions can alter the severity measure through alternative means.
+Figure \ref{fig:CC} displays an example graph that leverages color coding to easily identify problem states.
+\begin{figure}[htp]
+    \includegraphics[width=\linewidth]{"./Chapter3_img/CC.png"}
+    \vspace{.2truein} \centerline{}
+        \caption{Color Coding a Small Network Based on Violations}
+        \label{fig:CC}
+\end{figure}
+
 \TUsection{Compound Operators} \label{sec:compops}
 Many of the graphs previously generated by RAGE comprise of states with features that can be fully enumerated. In many of these generated graphs, there was an 
 established set of qualities that was used, with an established set of values. These typically have included $``compliance$\_$vio=true/false"$,
@@ -45,20 +60,16 @@ to make, since compound operators are conducted on numeric values, and matches t
 
 Other changes involved updating classes (namely the Quality, EncodedQuality, ParameterizedQuality, NetworkState, and Keyvalue classes) to include a new member for the operator in question. Auxiliary functions related to this new member, such as prints and getters, were also added. In addition, preconditions were altered to include operator overloads to check the asset identifier, quality name, and quality values for the update process.
 
-\TUsection{Color Coding}
+\TUsection{Relational Operators} \label{sec:relops}
-As a visual aid for analysis purposes, color coding was another feature implemented as a postprocessing tool for RAGE. When viewing the output graph of RAGE, all states are
+As discussed in Section \ref{sec:compops}, many of the graphs previously generated by RAGE comprise of states with an established set of qualities and values. These typically have included $``compliance$\_$vio=true/false"$,
-visibly identical in appearance apart from number of edges, edge IDs, and state IDs. To allow for visual differentiation, color coding can be enabled in the run script.
+$``root=true/false"$, or other general $``true/false"$ values or $``version=X"$ qualities. To further expand the dynamism of graph generation, it is important to distinguish when a quality has a value that satisfies a
-Color coding currently functions by working through the graph output text file, but it can be extended to read directly from the PostgreSQL database instead. The feature scans through the
+relational comparison to an exploit. An example application for attack graphs can be seen through CVE-2019-10747, where ``set-value is vulnerable to Prototype Pollution in versions lower than 3.0.1" \cite{CVE-2019-10747}. An example compliance graph application using the aforementioned car example can be seen in the Toyota Corolla Maintenance Schedule, which states an engine coolant replacement should be conducted after 24,000 miles. Prior to the implementation
-output file, and locates states that have $``compliance$\_$vios = X"$ (where \textit{X} is a number greater than 0), or $``compliance$\_$vio = true"$. For states that meet these
+of relational operators, to determine whether this exploit was applicable to a network state, multiple exploit qualities must be enumerated for all versions prior to 3.0.1. This would mean that the exploit needed to check if 
-properties, the color coding feature will add a color to the Graphviz DOT file through the $[color=COL]$ attribute for the given node, where \textit{COL} is assigned based on severity.
+\textit{version=3.0.0}, or \textit{version=2.0.0}, or \textit{version=1.0.0}, or \textit{version=0.4.3}, etc. For the compliance graph exploit check, this could lead to even worse scaling where checks needed to be conducted at a much more granular level like \textit{engine$\_$coolant$\_$miles=24001}, or \textit{engine$\_$coolant$\_$miles=24002}, or \textit{engine$\_$coolant$\_$miles=24003}, etc. This becomes increasingly tedious when there are many checks to perform, and this not only reduces readability, but is also more
-For this version of color coding, severity is determined by the total number of compliance violations a node has, but future versions can alter the severity measure through alternative means.
+prone to human error when creating the exploit files. Relational operators work to alleviate these difficulties.
-Figure \ref{fig:CC} displays an example graph that leverages color coding to easily identify problem states.
+
-\begin{figure}[htp]
+To implement the relational operators, operator overloads were placed into the Quality class. At the time of writing, the following are implemented: $==$, $<$, $>$, $\leq$, $\geq$. However, these operators do not take up room in the 
-    \includegraphics[width=\linewidth]{"./Chapter3_img/CC.png"}
+encoding scheme, so additional operators can be freely implemented as needed. The overloads ensure that the Quality asset IDs and Quality names match, and then compares the Quality values based on the operator in question.
-    \vspace{.2truein} \centerline{}
-        \caption{Color Coding a Small Network Based on Violations}
-        \label{fig:CC}
-\end{figure}
 
 \TUsection{Intermediate Database Storage}\label{sec:db-stor}
 \TUsubsection{Memory Constraint Difficulties}
@@ -111,13 +122,4 @@ performance benefits of memory operations, since graph computation relies less o
  request option), and the intermediate database storage process would function in the same fashion.
 
 
-\TUsection{Relational Operators} \label{sec:relops}
-As discussed in Section \ref{sec:compops}, many of the graphs previously generated by RAGE comprise of states with an established set of qualities and values. These typically have included $``compliance$\_$vio=true/false"$,
-$``root=true/false"$, or other general $``true/false"$ values or $``version=X"$ qualities. To further expand the dynamism of graph generation, it is important to distinguish when a quality has a value that satisfies a
-relational comparison to an exploit. An example application for attack graphs can be seen through CVE-2019-10747, where ``set-value is vulnerable to Prototype Pollution in versions lower than 3.0.1" \cite{CVE-2019-10747}. An example compliance graph application using the aforementioned car example can be seen in the Toyota Corolla Maintenance Schedule, which states an engine coolant replacement should be conducted after 24,000 miles. Prior to the implementation
-of relational operators, to determine whether this exploit was applicable to a network state, multiple exploit qualities must be enumerated for all versions prior to 3.0.1. This would mean that the exploit needed to check if 
-\textit{version=3.0.0}, or \textit{version=2.0.0}, or \textit{version=1.0.0}, or \textit{version=0.4.3}, etc. For the compliance graph exploit check, this could lead to even worse scaling where checks needed to be conducted at a much more granular level like \textit{engine$\_$coolant$\_$miles=24001}, or \textit{engine$\_$coolant$\_$miles=24002}, or \textit{engine$\_$coolant$\_$miles=24003}, etc. This becomes increasingly tedious when there are many checks to perform, and this not only reduces readability, but is also more
-prone to human error when creating the exploit files. Relational operators work to alleviate these difficulties.
 
-To implement the relational operators, operator overloads were placed into the Quality class. At the time of writing, the following are implemented: $==$, $<$, $>$, $\leq$, $\geq$. However, these operators do not take up room in the 
-encoding scheme, so additional operators can be freely implemented as needed. The overloads ensure that the Quality asset IDs and Quality names match, and then compares the Quality values based on the operator in question.

Chapter4.aux

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Chapter4.tex

@@ -25,7 +25,7 @@ from generating and exploring the unattainable states. Instead, a new feature ca
 The goal of the synchronous firing feature is to prevent the generation of unattainable states, while also not incurring a greater computational cost. This Chapter will discuss the development 
 of this feature, discuss its references and inspiration in literature, and examine the results when using this feature in applicable networks.
 
-\TUsubsection{Synchronous Firing in Literature} \label{sec:sync-lit}
+\TUsubsection{Related Synchronous Firing Work} \label{sec:sync-lit}
 A form of synchronous firing is discussed by the author of \cite{louthan_hybrid_2011}, where it is described as grouped exploits. The functionality discussed by the author is similar: firing
 an exploit should be performed on all possible assets simultaneously. This was also described as synchronizing multiple exploits. The methodology is similar to the one implemented in this 
 work, but there are notable differences. The first, is that the work performed by the author of \cite{louthan_hybrid_2011} utilizes global features with group features. Using the 
@@ -36,8 +36,8 @@ grouped exploit feature would attempt to fire all exploits on all applicable ass
 the applicable assets would. The last difference is that the work by the author of \cite{louthan_hybrid_2011} was developed in Python, since that was the language of the generator of the tool at the time.
 The work by the author of \cite{cook_rage_2018} led to new development of RAGE in C++ for performance enhancements, so the synchronous firing feature in this new work was likewise developed in C++.
 
-\TUsection{Necessary Alterations}
+\TUsection{Necessary Alterations and Additions}
-For the implementation of the synchronous firing feature, there were four primary changes that were necessary. The first is a change in the lexical analyzer, the second involves multiple changes
+For the implementation of the synchronous firing feature, there were four primary changes and additions that were necessary. The first is a change in the lexical analyzer, the second involves multiple changes
 to PostgreSQL, the third is the implementation of compound operators (as discussed in Section \ref{sec:compops}), and lastly is a change in the graph generation process. This Section will 
 consist of subsections discussing the development of these four alterations.
 
@@ -89,7 +89,7 @@ The implementation of synchronous firing in the graph generation process relies
 \end{figure}
 
 
-\TUsection{Example Networks and Results} \label{sec:test-platform}
+\TUsection{Experimental Networks and Results} \label{sec:test-platform}
 All data was collected on a 13 node cluster, with 12 nodes serving as dedicated compute nodes, and 1 node serving as the login node. Each compute node has a configuration as follows:
 \begin{itemize}
 \item{OS: CentOS release 6.9}
@@ -100,7 +100,7 @@ All data was collected on a 13 node cluster, with 12 nodes serving as dedicated
 \end{itemize}
 All nodes are connected with a 10Gbps Infiniband interconnect.
 
-\TUsubsection{Example Networks} \label{sec:Sync-Test}
+\TUsubsection{Experimental Networks} \label{sec:Sync-Test}
 The example networks for testing the effectiveness of synchronous firing follow the compliance graph generation approach. These networks analyze two assets, both of which are identical 2006 Toyota Corolla cars with identical qualities. The generation examines both cars at their current states, and proceeds to advance in time by a pre-determined amount, up to a pre-determined limit. Each time increment updates each car by an identical amount of mileage. During the generation process, it is determined if a car is out of compliance either through mileage or time since its last maintenance in accordance with the Toyota Corolla Maintenance Schedule manual.
 
 In addition, the tests employ the use of ``services", where if a car is out of compliance, it will go through a correction process and reset the mileage and time since last service. Each test varies in the number of services used. The 1 Service test only employs one service, and it is dedicated to brake pads. The 2 Service test employs two services, where the first service is dedicated to the brake pads, and the second is for exhaust pipes. This process extends to the 3 and 4 Service tests.

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Chapter5.tex

@@ -1,4 +1,4 @@
-\TUchapter{Utilization OF MESSAGE PASSING INTERFACE} \label{ch:MPI}
+\TUchapter{Parallelization Using MESSAGE PASSING INTERFACE} \label{ch:MPI}
 
 \TUsection{Introduction to MPI Utilization for Attack and Compliance Graph Generation}
 Previous works for graph generation, and specifically for attack graph generation, have seen promising results as discussed in Sections \ref{sec:gen_improv} and \ref{sec:related_works}. This work attempts to further those efforts and extend RAGE to function on distributed computing environments to take advantage of the increased computing power using message-passing. As mentioned by the author of \cite{pacheco_introduction_2011}, MPI is the most widely used message-passing API, and this work intended to utilize an API that was not only familiar and accessible, but versatile and powerful for parallelizing RAGE for distributed computing platforms. This Chapter discusses two approaches for parallelism: task parallelism in Section \ref{sec:Tasking-Approach}, and data parallelism in Section \ref{sec:Subgraphing_Approach}. All approaches in this work utilize OpenMPI for the MPI implementation.

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Schrick-Noah_MS-Thesis.lof

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Schrick-Noah_MS-Thesis.tex

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 %
 % Place the text of the abstract here
 %
-Attack graphs have historically been used to represent the state of a system or set of systems, and illustrate ways that attackers can carry out exploits to put the systems in a critical position. By refining the information and format of attack graphs, a similar process can be applied for compliance and regulation representations to illustrate ways that systems may violate or fall out of compliance in a format called compliance graphs.  Like attack graphs, compliance graphs also suffer from the state space explosion problem, and generated graphs quickly become very large even for small networks. This work introduces extensions to an attack graph generator, RAGE, to support compliance graph generation, and these extensions have led to successfully generating compliance graphs that can then be analyzed through an independent process. Additional extensions have been introduced to support the utility of RAGE, and this includes the implementation of a synchronous firing feature to prevent generation of states where assets deviate from a shared, inseparable feature such as time. The attack graph generation algorithm has also been modified by two different approaches to expand the generation process to function for distributed computing environments using Message Passing Interface (MPI).
+Attack graphs have historically been used to represent the state of a system or set of systems, and illustrate ways that attackers can carry out exploits to put the systems in a critical position. By refining the information and format of attack graphs, a similar process can be applied for compliance and regulation representations to illustrate ways that systems may violate or fall out of compliance in a format called compliance graphs.  Like attack graphs, compliance graphs also suffer from the state space explosion problem, and generated graphs quickly become very large even for small networks. This work introduces extensions to an attack graph generator, RAGE attack graph engine (RAGE), to support compliance graph generation, and these extensions have led to successfully generating compliance graphs that can then be analyzed through an independent process. Additional extensions have been introduced to support the utility of RAGE, and this includes the implementation of a synchronous firing feature to prevent generation of states where assets deviate from a shared, inseparable feature such as time. The compliance graph generation algorithm has also been modified by two different approaches to expand the generation process to function for distributed computing environments using Message Passing Interface (MPI).
 
 \acknowledgementsp
 %

Schrick-Noah_MS-Thesis.toc

@@ -5,11 +5,11 @@
 {\hfill \ }
 \contentsline {section}{\hspace {-\parindent }ACKNOWLEDGEMENTS}{v}{}%
 {\hfill \ }
-\contentsline {section}{\hspace {-\parindent }TABLE OF CONTENTS}{vii}{}%
+\contentsline {section}{\hspace {-\parindent }TABLE OF CONTENTS}{viii}{}%
 {\hfill \ }
-\contentsline {section}{\hspace {-\parindent }LIST OF TABLES}{viii}{}%
+\contentsline {section}{\hspace {-\parindent }LIST OF TABLES}{ix}{}%
 {\hfill \ }
-\contentsline {section}{\hspace {-\parindent }LIST OF FIGURES}{x}{}%
+\contentsline {section}{\hspace {-\parindent }LIST OF FIGURES}{xi}{}%
 \contentsline {chapter}{\numberline {CHAPTER 1: }{\bf \uppercase {INTRODUCTION}}}{1}{}%
 \contentsline {section}{\numberline {1.1}\bf Introduction to Attack Graphs}{1}{}%
 \contentsline {section}{\numberline {1.2}\bf Application to Compliance}{2}{}%
@@ -23,47 +23,47 @@
 \contentsline {section}{\numberline {2.3}\bf Improvements Specific to Attack Graph Generation}{6}{}%
 \contentsline {chapter}{\numberline {CHAPTER 3: }{\bf \uppercase {UTILITY EXTENSIONS TO THE RAGE ATTACK GRAPH GENERATOR}}}{8}{}%
 \contentsline {section}{\numberline {3.1}\bf Path Walking}{8}{}%
-\contentsline {section}{\numberline {3.2}\bf Compound Operators}{8}{}%
+\contentsline {section}{\numberline {3.2}\bf Color Coding}{9}{}%
-\contentsline {section}{\numberline {3.3}\bf Color Coding}{10}{}%
+\contentsline {section}{\numberline {3.3}\bf Compound Operators}{11}{}%
-\contentsline {section}{\numberline {3.4}\bf Intermediate Database Storage}{11}{}%
+\contentsline {section}{\numberline {3.4}\bf Relational Operators}{12}{}%
-\contentsline {subsection}{\numberline {3.4.1}\it Memory Constraint Difficulties}{11}{}%
+\contentsline {section}{\numberline {3.5}\bf Intermediate Database Storage}{13}{}%
-\contentsline {subsection}{\numberline {3.4.2}\it Maximizing Performance with Intermediate Database Storage}{13}{}%
+\contentsline {subsection}{\numberline {3.5.1}\it Memory Constraint Difficulties}{13}{}%
-\contentsline {subsection}{\numberline {3.4.3}\it Portability}{15}{}%
+\contentsline {subsection}{\numberline {3.5.2}\it Maximizing Performance with Intermediate Database Storage}{14}{}%
-\contentsline {section}{\numberline {3.5}\bf Relational Operators}{15}{}%
+\contentsline {subsection}{\numberline {3.5.3}\it Portability}{16}{}%
 \contentsline {chapter}{\numberline {CHAPTER 4: }{\bf \uppercase {SYNCHRONOUS FIRING}}}{17}{}%
 \contentsline {section}{\numberline {4.1}\bf Introduction}{17}{}%
-\contentsline {subsection}{\numberline {4.1.1}\it Synchronous Firing in Literature}{18}{}%
+\contentsline {subsection}{\numberline {4.1.1}\it Related Synchronous Firing Work}{18}{}%
-\contentsline {section}{\numberline {4.2}\bf Necessary Alterations}{19}{}%
+\contentsline {section}{\numberline {4.2}\bf Necessary Alterations and Additions}{19}{}%
 \contentsline {subsection}{\numberline {4.2.1}\it GNU Bison and Flex}{19}{}%
 \contentsline {subsection}{\numberline {4.2.2}\it PostgreSQL}{20}{}%
 \contentsline {subsection}{\numberline {4.2.3}\it Compound Operators}{21}{}%
 \contentsline {subsection}{\numberline {4.2.4}\it Graph Generation}{21}{}%
-\contentsline {section}{\numberline {4.3}\bf Example Networks and Results}{22}{}%
+\contentsline {section}{\numberline {4.3}\bf Experimental Networks and Results}{22}{}%
-\contentsline {subsection}{\numberline {4.3.1}\it Example Networks}{22}{}%
+\contentsline {subsection}{\numberline {4.3.1}\it Experimental Networks}{22}{}%
 \contentsline {subsection}{\numberline {4.3.2}\it Results}{24}{}%
-\contentsline {chapter}{\numberline {CHAPTER 5: }{\bf \uppercase {Utilization OF MESSAGE PASSING INTERFACE}}}{28}{}%
+\contentsline {chapter}{\numberline {CHAPTER 5: }{\bf \uppercase {Parallelization Using MESSAGE PASSING INTERFACE}}}{28}{}%
 \contentsline {section}{\numberline {5.1}\bf Introduction to MPI Utilization for Attack and Compliance Graph Generation}{28}{}%
 \contentsline {section}{\numberline {5.2}\bf Necessary Components}{28}{}%
 \contentsline {subsection}{\numberline {5.2.1}\it Serialization}{28}{}%
 \contentsline {section}{\numberline {5.3}\bf Tasking Approach}{29}{}%
 \contentsline {subsection}{\numberline {5.3.1}\it Introduction to the Tasking Approach}{29}{}%
 \contentsline {subsection}{\numberline {5.3.2}\it Algorithm Design}{31}{}%
-\contentsline {subsubsection}{Communication Structure}{31}{}%
+\contentsline {subsubsection}{\numberline {5.3.2.1}Communication Structure}{31}{}%
-\contentsline {subsubsection}{Task 0}{33}{}%
+\contentsline {subsubsection}{\numberline {5.3.2.2}Task 0}{33}{}%
-\contentsline {subsubsection}{Task 1}{33}{}%
+\contentsline {subsubsection}{\numberline {5.3.2.3}Task 1}{33}{}%
-\contentsline {subsubsection}{Task 2}{34}{}%
+\contentsline {subsubsection}{\numberline {5.3.2.4}Task 2}{34}{}%
-\contentsline {subsubsection}{Task 3}{34}{}%
+\contentsline {subsubsection}{\numberline {5.3.2.5}Task 3}{34}{}%
-\contentsline {subsubsection}{Task 4 and Task 5}{36}{}%
+\contentsline {subsubsection}{\numberline {5.3.2.6}Task 4 and Task 5}{36}{}%
-\contentsline {subsubsection}{MPI Tags}{37}{}%
+\contentsline {subsubsection}{\numberline {5.3.2.7}MPI Tags}{37}{}%
 \contentsline {subsection}{\numberline {5.3.3}\it Performance Expectations and Use Cases}{37}{}%
 \contentsline {subsection}{\numberline {5.3.4}\it Results}{38}{}%
-\contentsline {section}{\numberline {5.4}\bf Subgraphing Approach}{41}{}%
+\contentsline {section}{\numberline {5.4}\bf Subgraphing Approach}{39}{}%
 \contentsline {subsection}{\numberline {5.4.1}\it Introduction to the Subgraphing Approach}{41}{}%
 \contentsline {subsection}{\numberline {5.4.2}\it Algorithm Design}{41}{}%
-\contentsline {subsubsection}{Worker Nodes}{42}{}%
+\contentsline {subsubsection}{\numberline {5.4.2.1}Worker Nodes}{42}{}%
-\contentsline {subsubsection}{Root Node}{43}{}%
+\contentsline {subsubsection}{\numberline {5.4.2.2}Root Node}{43}{}%
-\contentsline {subsubsection}{Database Node}{44}{}%
+\contentsline {subsubsection}{\numberline {5.4.2.3}Database Node}{44}{}%
-\contentsline {subsubsection}{MPI Tags}{44}{}%
+\contentsline {subsubsection}{\numberline {5.4.2.4}MPI Tags}{44}{}%
 \contentsline {subsection}{\numberline {5.4.3}\it Performance Expectations and Use Cases}{44}{}%
 \contentsline {subsection}{\numberline {5.4.4}\it Results}{45}{}%
 \contentsline {chapter}{\numberline {CHAPTER 6: }{\bf \uppercase {CONCLUSIONS AND FUTURE WORKS}}}{54}{}%

TUthesis.sty

@@ -141,8 +141,8 @@
 % chapters, sections, subsections and subsub sections, but only the chapters and sections 
 % will be numbered.
 %
-\setcounter{tocdepth}{3}
+\setcounter{tocdepth}{4}
-\setcounter{secnumdepth}{2}
+\setcounter{secnumdepth}{4}
 
 %%%%%%%%%%%%%%%%%%%%%%%
 % Test box for margins
@@ -586,6 +586,8 @@
  -.2ex}{0.1ex plus .2ex}{\normalsize\it}}
 \def\subsubsection{\@startsection{subsubsection}{3}{\parindent}{-3.25ex plus -1ex minus 
  -.2ex}{-0.1ex plus .2ex}{\normalsize\rm}}
+%\def\subsection{\@startsection{subsubsection}{3}{\z@}{-3.25ex plus -1ex minus 
+% -.2ex}{0.1ex plus .2ex}{\normalsize\it}}
 \def\paragraph{\@startsection{paragraph}{4}{\z@}{3.25ex plus 1ex minus .2ex}{-1em}{\normalsize\bf}}
 \def\subparagraph{\@startsection
  {subparagraph}{4}{\parindent}{3.25ex plus 1ex minus