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74
Bibliography.bib
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@ -136,3 +136,77 @@
year = {2018},
file = {Will_Nichols_Thesis_FINAL_VER:/home/noah/Zotero/storage/8AXSZXJN/Will_Nichols_Thesis_FINAL_VER.pdf:application/pdf},
}
@article{ming_jo,
author = {Li, Ming and Hawrylak, Peter and Hale, John},
title = {Strategies for Practical Hybrid Attack Graph Generation and Analysis},
year = {2021},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
issn = {2692-1626},
url = {https://doi.org/10.1145/3491257},
doi = {10.1145/3491257},
abstract = {As an analytical tool in cyber-security, an attack graph (AG) is capable of discovering multi-stage attack vectors on target computer networks. Cyber-physical systems (CPSs) comprise a special type of network that not only contains computing devices but also integrates components that operate in the continuous domain, such as sensors and actuators. Using AGs on CPSs requires that the system models and exploit patterns capture both token- and real-valued information. In this paper, we describe a hybrid AG model for security analysis of CPSs and computer networks. Specifically, we focus on two issues related to applying the model in practice: efficient hybrid AG generation and techniques for information extraction from them. To address the first issue, we present an accelerated hybrid AG generator that employs parallel programming and high performance computing (HPC). We conduct performance tests on CPU and GPU platforms to characterize the efficiency of our parallel algorithms. To address the second issue, we introduce an analytical regimen based on centrality analysis and apply it to a hybrid AG generated for a target CPS system to discover effective vulnerability remediation solutions.},
note = {Just Accepted},
journal = {Digital Threats},
month = {oct},
keywords = {cyber-physical system, high performance computing, attack graph, breadth-first search}
}
@inproceedings{CPSIOT,
author = {Al Ghazo, Alaa T. and Ibrahim, Mariam and Ren, Hao and Kumar, Ratnesh},
title = {A2G2V: Automated Attack Graph Generator and Visualizer},
year = {2018},
isbn = {9781450358606},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3215466.3215468},
doi = {10.1145/3215466.3215468},
abstract = {The Internet of Things (IoT) and Cyber-Physical Systems (CPS) technologies have increased the complexity of systems and also exposed them to additional vulnerabilities. Attack-graphs are graphical representations that provide a complete view of how inter-dependencies among atomic vulnerabilities may be exploited by an adversary to stitch together an attack that can compromise the system. Their manual construction is tedious, error-prone, and time consuming. This paper presents a model-based Automated Attack-Graph Generator and Visualizer (A2G2V). Given the networked system description (its components, connectivity, services it supports, their vulnerabilities and protections), the attack graph enlists set of all possible sequences in which atomic-level vulnerabilities can be exploited to compromise a certain system-level security. The proposed A2G2V tool extends an existing formal methods tool (a model-checker) by integrating with it an architecture description tool, our own code (for parsing counterexamples, encoding those for specification relaxation, iterating till all attack sequences are revealed), and also a graph visualization tool.},
booktitle = {Proceedings of the 1st ACM MobiHoc Workshop on Mobile IoT Sensing, Security, and Privacy},
articleno = {3},
numpages = {6},
keywords = {Model Checking, Security, Enumerating Counterexamples, Internet of Things, Attack Graph, Cyber-Physical Systems},
location = {Los Angeles, CA, USA},
series = {Mobile IoT SSP'18}
}
@article{10.1145/3105760,
author = {Mu\~{n}oz-Gonz\'{a}lez, Luis and Sgandurra, Daniele and Paudice, Andrea and Lupu, Emil C.},
title = {Efficient Attack Graph Analysis through Approximate Inference},
year = {2017},
issue_date = {August 2017},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {20},
number = {3},
issn = {2471-2566},
url = {https://doi.org/10.1145/3105760},
doi = {10.1145/3105760},
abstract = {Attack graphs provide compact representations of the attack paths an attacker can follow to compromise network resources from the analysis of network vulnerabilities and topology. These representations are a powerful tool for security risk assessment. Bayesian inference on attack graphs enables the estimation of the risk of compromise to the system’s components given their vulnerabilities and interconnections and accounts for multi-step attacks spreading through the system. While static analysis considers the risk posture at rest, dynamic analysis also accounts for evidence of compromise, for example, from Security Information and Event Management software or forensic investigation. However, in this context, exact Bayesian inference techniques do not scale well. In this article, we show how Loopy Belief Propagation—an approximate inference technique—can be applied to attack graphs and that it scales linearly in the number of nodes for both static and dynamic analysis, making such analyses viable for larger networks. We experiment with different topologies and network clustering on synthetic Bayesian attack graphs with thousands of nodes to show that the algorithm’s accuracy is acceptable and that it converges to a stable solution. We compare sequential and parallel versions of Loopy Belief Propagation with exact inference techniques for both static and dynamic analysis, showing the advantages and gains of approximate inference techniques when scaling to larger attack graphs.},
journal = {ACM Trans. Priv. Secur.},
month = {jul},
articleno = {10},
numpages = {30},
keywords = {probabilistic graphical models, approximate inference, Bayesian networks}
}
@ARTICLE{8290918,
author={Wang, Huan and Chen, Zhanfang and Zhao, Jianping and Di, Xiaoqiang and Liu, Dan},
journal={IEEE Access},
title={A Vulnerability Assessment Method in Industrial Internet of Things Based on Attack Graph and Maximum Flow},
year={2018},
volume={6},
number={},
pages={8599-8609},
doi={10.1109/ACCESS.2018.2805690}
}
@inproceedings{centrality_based,
author = {Gonda, Tom and Pascal, Tal and Puzis, Rami and Shani, Guy and Shapira, Bracha},
year = {2018},
month = {09},
pages = {},
title = {Analysis of Attack Graph Representations for Ranking Vulnerability Fixes},
doi = {10.29007/2c1q}
}

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@ -21,6 +21,11 @@
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\bibcite{zhang_boosting_2017}{8}
\bibcite{Monotonicity}{9}
\bibcite{TVA}{10}
\bibcite{louthan_hybrid_2011}{11}
\bibcite{cook_rage_2018}{12}
\bibcite{nichols_2018}{13}
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Schrick-Noah_AG-CG-SyncFire.bbl
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@ -15,6 +15,31 @@ X.~Ou, W.~F. Boyer, and M.~A. Mcqueen, ``A {Scalable} {Approach} to {Attack}
{Graph} {Generation},'' {\em CCS '06: Proceedings of the 13th ACM conference
on Computer and communications security}, pp.~336--345, 2006.
\bibitem{CPSIOT}
A.~T. Al~Ghazo, M.~Ibrahim, H.~Ren, and R.~Kumar, ``A2g2v: Automated attack
graph generator and visualizer,'' in {\em Proceedings of the 1st ACM MobiHoc
Workshop on Mobile IoT Sensing, Security, and Privacy}, Mobile IoT SSP'18,
(New York, NY, USA), Association for Computing Machinery, 2018.
\bibitem{ming_jo}
M.~Li, P.~Hawrylak, and J.~Hale, ``Strategies for practical hybrid attack graph
generation and analysis,'' {\em Digital Threats}, oct 2021.
\newblock Just Accepted.
\bibitem{10.1145/3105760}
L.~Mu\~{n}oz Gonz\'{a}lez, D.~Sgandurra, A.~Paudice, and E.~C. Lupu,
``Efficient attack graph analysis through approximate inference,'' {\em ACM
Trans. Priv. Secur.}, vol.~20, jul 2017.
\bibitem{8290918}
H.~Wang, Z.~Chen, J.~Zhao, X.~Di, and D.~Liu, ``A vulnerability assessment
method in industrial internet of things based on attack graph and maximum
flow,'' {\em IEEE Access}, vol.~6, pp.~8599--8609, 2018.
\bibitem{centrality_based}
T.~Gonda, T.~Pascal, R.~Puzis, G.~Shani, and B.~Shapira, ``Analysis of attack
graph representations for ranking vulnerability fixes,'' 09 2018.
\bibitem{j_hale_compliance_nodate}
{J. Hale}, P.~Hawrylak, and M.~Papa, ``Compliance {Method} for a
{Cyber}-{Physical} {System}.''

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[]\OT1/ptm/m/n/10 Introducing service heuristics could improve the
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[]\OT1/ptm/m/n/8 G. Louthan, \OT1/ptm/m/it/8 Hybrid Attack Graphs for Modeling
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@ -63,10 +63,10 @@ Attack Graph; Compliance Graph; Synchronous Firing; High-Performance Computing;
\section{Introduction}
Cybersecurity has been at the forefront of computing for decades, and vulnerability analysis modeling has been utilized to mitigate threats to aid in this effort. One such modeling approach is to represent a system or a set of systems through graphical means, and encode information into the nodes and edges of the graph. Even as early as the late 1990s, experts have composed various graphical models to map devices and vulnerabilities through attack trees, and this work can be seen through the works published by the authors of \cite{phillips_graph-based_1998}.
This work, and other attack tree discussions of this time such as that conducted by the author of \cite{schneier_modeling_1999}, would later be referred to as early versions of modern-day attack graphs \cite{ou_scalable_2006}.
By utilizing this graphical approach, cybersecurity postures can be measured at a system's current status, as well as hypothesize and examine other postures based on system changes over time.
By utilizing this graphical approach, cybersecurity postures can be measured at a system's current status, as well as hypothesize and examine other postures based on system changes over time. Attack graphs have also been extended to Cyber-Physical Systems (CPS) and the Internet of Things (IoT), and their usage can be seen in works such as that presented by the authors of \cite{CPSIOT} and the authors of \cite{ming_jo}. Various analysis metrics can then be performed, such as Bayesian attack graphs \cite{10.1145/3105760}, maximum flow \cite{8290918}, and centrality-based ranking measures \cite{centrality_based}.
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 such as critical infrastructure and Internet of Things. The challenge of
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 such as critical infrastructure and IoT. 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, 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.
@ -103,7 +103,6 @@ Post-processing is one option at removing the unattainable states. This process
Instead, a new feature called synchronous firing can be used to prevent the generation of these states. The goal of the synchronous firing feature is to prevent the generation of unattainable states, while also not incurring a greater computational cost. Section \ref{sec:implementing} will discuss the development of this feature, and Section \ref{sec:Results} will examine the results when using this feature in applicable networks.
\section{Implementing Synchronous Firing} \label{sec:implementing}
\subsection{Base Generator Description}
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, and lastly is a change in the graph generation process. This Section will consist of subsections discussing the development of these four alterations.
\subsection{GNU Bison and Flex}
@ -366,6 +365,8 @@ As seen and discussed in Section \ref{sec:inseparable}, when unattainable states
Another avenue for future works would be to take a network science approach. There may be features of interest from examining the topology of the resulting graphs with and without synchronous firing. Various centrality metrics could be examined, as well as examining transformations such as dominant trees or transitive closures derived from the original graphs. Each approach could compare each graph when using or not using synchronous firing to determine if there are possible points of interest. Taking a network science approach could also examine and analyze the E/S Ratio differences between the graphs when using or not using synchronous firing, and attempt to provide further insight on what those differences mean in terms of usability of the graphs.
Introducing service heuristics could improve the characteristics of synchronous firing. If services are performed too early, then additional states would be generated in the resulting graph. If synchronous firing was not used, these additional states could compound into more states due to the separation of features. Likewise, if services are performed too late, then additional states could be generated to represent the compliance violation, and these states may also compound into more statues without synchronous firing. Examining the impact of synchronous firing when various heuristics are implemented could reveal interesting results.
\section{Conclusion}
This work implemented a state space explosion mitigation technique called synchronous firing. This feature is able to fire exploits simultaneously among a group of assets through a single state transition. By firing exploits across multiple assets, it is able to prevent the separation of features that should normally be inseparable (such as time), and successfully reduces the number of total states in the resulting attack or compliance graph. This feature does not alter the procedure of the generation process in a way that undermines the integrity of the resulting attack or compliance graph, and only groups assets through defined inseparable features. This feature is also toggleable, and the generation process seen in Figure \ref{fig:sync-fire} does not change if the feature is disabled. This feature successfully reduced the total number of states, reduced the runtime of the generation process, and can lead to a reduced analysis process due to a smaller resulting graph.

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