@misc{j_hale_compliance_nodate,
	title = {Compliance {Method} for a {Cyber}-{Physical} {System}},
	author = {{J. Hale} and Hawrylak, P. and Papa, M.},
	note = {U.S. Patent Number 9,471,789, Oct. 18, 2016.},
	number = {9471789},
	file = {Complaince_Graph_US_Patent_9471789:/home/noah/Zotero/storage/55BZN4U7/Complaince_Graph_US_Patent_9471789.pdf:application/pdf},
}

@article{li_combining_2019,
	title = {Combining {OpenCL} and {MPI} to support heterogeneous computing on a cluster},
	issn = {9781450372275},
	doi = {10.1145/3332186.3333059},
	abstract = {This paper presents an implementation of a heterogeneous programming model which combines Open Computing Language (OpenCL) and Message Passing Interface (MPI). The model is applied to solving a Markov decision process (MDP) with value iteration method. The performance test is conducted on a high performance computing cluster. At peak performance, the model is able to achieve a 57X speedup over a serial implementation. For an extremely large input MDP, which has 1,000,000 states, the obtained speedup is still over 12X, showing that this heterogeneous programming model can solve MDPs more efficiently than the serial solver does.},
	journal = {ACM International Conference Proceeding Series},
	author = {Li, Ming and Hawrylak, Peter and Hale, John},
	year = {2019},
	keywords = {Heterogeneous computing, HPC, MDP, MPI, OpenCL, Parallelism},
	file = {Combining OpenCL and MPI to Support Heterogeneous Computing on a Cluster:/home/noah/Zotero/storage/TXHCQ5S8/Combining OpenCL and MPI to Support Heterogeneous Computing on a Cluster.pdf:application/pdf},
}

@mastersthesis{zeng_cyber_2017,
	title = {Cyber {Attack} {Analysis} {Based}   on {Markov} {Process} {Model}},
	author = {Zeng, Keming},
	school = "The University of Tulsa",
	year = {2017},
	address = "Tulsa, OK",
}

@inproceedings{baloyi_guidelines_2019,
	address = {Skukuza South Africa},
	title = {Guidelines for {Data} {Privacy} {Compliance}: {A} {Focus} on {Cyberphysical} {Systems} and {Internet} of {Things}},
	doi = {10.1145/3351108.3351143},
	booktitle = {{SAICSIT} '19: {Proceedings} of the {South} {African} {Institute} of {Computer} {Scientists} and {Information} {Technologists} 2019},
	publisher = {Association for Computing Machinery},
	author = {Baloyi, Ntsako and Kotzé, Paula},
	year = {2019},
}

@article{allman_complying_2006,
	title = {Complying with {Compliance}: {Blowing} it off is not an option.},
	volume = {4},
	number = {7},
	journal = {ACM Queue},
	author = {Allman, Eric},
	year = {2006},
}

@misc{noauthor_health_1996,
	title = {Health {Insurance} {Portability} and {Accountability} {Act} of 1996},
	note = {Pub. L. No. 104-191. 1996 [Online]. Available: https://www.govinfo.gov/content/pkg/PLAW-104publ191/html/PLAW-104publ191.htm},
}

@misc{PCI,
	title = {Payment {Card} {Industry} {(PCI)} {Data} {Security} {Standard}},
	note = {{Available: https://www.pcisecuritystandards.org/documents/PCI$\_$DSS$\_$v3-2-1.pdf}},
	month = may,
	year = {2018},
	author = {PCI Security Standards Council}
}

@article{centrality_causal,
	title = {Node centrality measures are a poor substitute for causal inference},
	volume = {9},
	issn = {6846},
	doi = {10.1038/s41598-019-43033-9},
	journal = {Scientific Reports},
	author = {Dablander, Fabian and Hinne, Max},
	year = {2019},
}

@inproceedings{Mieghem2018DirectedGA,
  title={Directed graphs and mysterious complex eigenvalues},
  author={Piet Van Mieghem},
  year={2018}
}

@article{Guo2017HermitianAM,
  title={Hermitian Adjacency Matrix of Digraphs and Mixed Graphs},
  author={Krystal Guo and Bojan Mohar},
  journal={Journal of Graph Theory},
  year={2017},
  volume={85}
}

@article{Brualdi2010SpectraOD,
  title={Spectra of digraphs},
  author={Richard A. Brualdi},
  journal={Linear Algebra and its Applications},
  year={2010},
  volume={432},
  pages={2181-2213}
}

@article {PMID:30064421,
	Title = {A systematic survey of centrality measures for protein-protein interaction networks},
	Author = {Ashtiani, Minoo and Salehzadeh-Yazdi, Ali and Razaghi-Moghadam, Zahra and Hennig, Holger and Wolkenhauer, Olaf and Mirzaie, Mehdi and Jafari, Mohieddin},
	DOI = {10.1186/s12918-018-0598-2},
	Number = {1},
	Volume = {12},
	Month = {July},
	Year = {2018},
	Journal = {BMC systems biology},
	ISSN = {1752-0509},
	Pages = {80},
	URL = {https://europepmc.org/articles/PMC6069823},
}

@Article{Katz,
  author={Leo Katz},
  title={{A new status index derived from sociometric analysis}},
  journal={Psychometrika},
  year=1953,
  volume={18},
  number={1},
  pages={39-43},
  month={March},
  keywords={},
  doi={10.1007/BF02289026},
  abstract={No abstract is available for this item.},
  url={https://ideas.repec.org/a/spr/psycho/v18y1953i1p39-43.html}
}

@article{ModKatz,
  title={Katz centrality of Markovian temporal networks: Analysis and optimization},
  author={Masaki Ogura and Victor M. Preciado},
  journal={2017 American Control Conference (ACC)},
  year={2017},
  pages={5001-5006}
}

@book{newman2010networks,
  title={Networks: An Introduction},
  author={Newman, M.E.J.},
  isbn={9780191594175},
  url={https://books.google.com/books?id=sgSlvgEACAAJ},
  year={2010},
  publisher={Oxford University Press}
}

@article{K_Path_Edge,
	doi = {10.1016/j.knosys.2012.01.007},
	url = {https://doi.org/10.1016%2Fj.knosys.2012.01.007},
	year = 2012,
	month = {jun},
	publisher = {Elsevier {BV}},
	volume = {30},
	pages = {136--150},
	author = {Pasquale De Meo and Emilio Ferrara and Giacomo Fiumara and Angela Ricciardello},
	title = {A novel measure of edge centrality in social networks},
	journal = {Knowledge-Based Systems}
}

@article{Adapted_PageRank,
  title={An algorithm for ranking the nodes of an urban network based on the concept of PageRank vector},
  author={Taras Agryzkov and Jos{\'e} Luis Oliver and Leandro Tortosa and Jos{\'e}-Francisco Vicent},
  journal={Appl. Math. Comput.},
  year={2012},
  volume={219},
  pages={2186-2193}
}

@article{PageRank,
	title = {The anatomy of a large-scale hypertextual Web search engine},
	journal = {Computer Networks and ISDN Systems},
	volume = {30},
	number = {1},
	pages = {107-117},
	year = {1998},
	note = {Proceedings of the Seventh International World Wide Web Conference},
	issn = {0169-7552},
	doi = {https://doi.org/10.1016/S0169-7552(98)00110-X},
	url = {https://www.sciencedirect.com/science/article/pii/S016975529800110X},
	author = {Sergey Brin and Lawrence Page},
	keywords = {World Wide Web, Search engines, Information retrieval, PageRank, Google},
	abstract = {In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24 million pages is available at http://google.stanford.edu/ To engineer a search engine is a challenging task. Search engines index tens to hundreds of millions of Web pages involving a comparable number of distinct terms. They answer tens of millions of queries every day. Despite the importance of large-scale search engines on the Web, very little academic research has been done on them. Furthermore, due to rapid advance in technology and Web proliferation, creating a Web search engine today is very different from three years ago. This paper provides an in-depth description of our large-scale Web search engine — the first such detailed public description we know of to date. Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technical challenges involved with using the additional information present in hypertext to produce better search results. This paper addresses this question of how to build a practical large-scale system which can exploit the additional information present in hypertext. Also we look at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.}
}

@article{PageRank_Survey,
	author = { Pavel   Berkhin },
	title = {A Survey on PageRank Computing},
	journal = {Internet Mathematics},
	volume = {2},
	number = {1},
	pages = {73-120},
	year  = {2005},
	publisher = "Taylor \& Francis",
	doi = {10.1080/15427951.2005.10129098},
	URL = {https://doi.org/10.1080/15427951.2005.10129098},
	eprint = {https://doi.org/10.1080/15427951.2005.10129098}
}

@inproceedings{dominance,
	author = {Prosser, Reese T.},
	title = {Applications of Boolean Matrices to the Analysis of Flow Diagrams},
	year = {1959},
	isbn = {9781450378680},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	url = {https://doi.org/10.1145/1460299.1460314},
	doi = {10.1145/1460299.1460314},
	abstract = {Any serious attempt at automatic programming of large-scale digital computing machines must provide for some sort of analysis of program structure. Questions concerning order of operations, location and disposition of transfers, identification of subroutines, internal consistency, redundancy and equivalence, all involve a knowledge of the structure of the program under study, and must be handled effectively by any automatic programming system.},
	booktitle = {Papers Presented at the December 1-3, 1959, Eastern Joint IRE-AIEE-ACM Computer Conference},
	pages = {133–138},
	numpages = {6},
	location = {Boston, Massachusetts},
	series = {IRE-AIEE-ACM '59 (Eastern)}
}

@article{10.1145/3491257,
	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.},
	journal = {Digital Threats},
	month = {oct},
	keywords = {attack graph, breadth-first search, cyber-physical system, high performance computing}
}

@article{ZENITANI2023103081,
	title = {Attack graph analysis: An explanatory guide},
	journal = "Computers \& Security",
	volume = {126},
	pages = {103081},
	year = {2023},
	issn = {0167-4048},
	doi = {https://doi.org/10.1016/j.cose.2022.103081},
	url = {https://www.sciencedirect.com/science/article/pii/S0167404822004734},
	author = {Kengo Zenitani},
	keywords = {Attack graph, Exploit dependency graph, Cycle handling, Network security metrics, Network hardening, Bayesian attack graph},
	abstract = {Attack graph analysis is a model-based approach for network-security analysis. It analyzes a directed graph called an attack graph. Usually, each node in it corresponds to a malicious event caused by attackers, and the edges correspond to the causal relations between events. We can obtain an attack graph from the network topology, its configuration, and the distribution of vulnerabilities. An attack graph gives us various information relevant to network security. Also, there are several relevant algorithms to find desirable security controls applicable to the network. Over twenty years of research have made much progress in this field. However, it comprises a breadth of definitions and discussions, and it is difficult for people new to this field to comprehend the key ideas. This article aims to briefly introduce this method to prospective researchers by summarizing their progress by selecting and reviewing foundational studies. We elaborate on the essential concepts, such as exploit dependency, AND/OR graph, monotonicity, and cycle handling.}
}

@article{Zeng2019SurveyOA,
  title={Survey of Attack Graph Analysis Methods from the Perspective of Data and Knowledge Processing},
  author={Jianping Zeng and Shuang Wu and Yanyu Chen and Rui Zeng and Chengrong Wu},
  journal={Secur. Commun. Networks},
  year={2019},
  volume={2019},
  pages={2031063:1-2031063:16}
}

@phdthesis{ming_diss,
	author = {Li, Ming and Hawrylak, Peter and Hale, John},
	title   = "A System for Attack Graph Generation and Analysis",
	school  = "The University of Tulsa",
	year    = "2021",
	type    = "{PhD} dissertation",
	address = "Tulsa, OK",
}

@article{MO2019121538,
title = {Identifying node importance based on evidence theory in complex networks},
journal = {Physica A: Statistical Mechanics and its Applications},
volume = {529},
pages = {121538},
year = {2019},
issn = {0378-4371},
doi = {https://doi.org/10.1016/j.physa.2019.121538},
url = {https://www.sciencedirect.com/science/article/pii/S0378437119309021},
author = {Hongming Mo and Yong Deng},
keywords = {Complex networks, Important nodes, Evidence theory, Multi-evidence centrality, Comprehensive measure},
}

@article{LI2018512,
title = {Identification of influential spreaders based on classified neighbors in real-world complex networks},
journal = {Applied Mathematics and Computation},
volume = {320},
pages = {512-523},
year = {2018},
issn = {0096-3003},
doi = {https://doi.org/10.1016/j.amc.2017.10.001},
url = {https://www.sciencedirect.com/science/article/pii/S0096300317306884},
author = {Chao Li and Li Wang and Shiwen Sun and Chengyi Xia},
keywords = {Influential spreaders, Identification algorithms, Classified neighbors, Complex networks},
}


@Article{sym11020284,
AUTHOR = {Agryzkov, Taras and Curado, Manuel and Pedroche, Francisco and Tortosa, Leandro and Vicent, José F.},
TITLE = {Extending the Adapted PageRank Algorithm Centrality to Multiplex Networks with Data Using the PageRank Two-Layer Approach},
JOURNAL = {Symmetry},
VOLUME = {11},
YEAR = {2019},
NUMBER = {2},
ARTICLE-NUMBER = {284},
URL = {https://www.mdpi.com/2073-8994/11/2/284},
ISSN = {2073-8994},
ABSTRACT = {Usually, the nodes&rsquo; interactions in many complex networks need a more accurate mapping than simple links. For instance, in social networks, it may be possible to consider different relationships between people. This implies the use of different layers where the nodes are preserved and the relationships are diverse, that is, multiplex networks or biplex networks, for two layers. One major issue in complex networks is the centrality, which aims to classify the most relevant elements in a given system. One of these classic measures of centrality is based on the PageRank classification vector used initially in the Google search engine to order web pages. The PageRank model may be understood as a two-layer network where one layer represents the topology of the network and the other layer is related to teleportation between the nodes. This approach may be extended to define a centrality index for multiplex networks based on the PageRank vector concept. On the other hand, the adapted PageRank algorithm (APA) centrality constitutes a model to obtain the importance of the nodes in a spatial network with the presence of data (both real and virtual). Following the idea of the two-layer approach for PageRank centrality, we can consider the APA centrality under the perspective of a two-layer network where, on the one hand, we keep maintaining the layer of the topological connections of the nodes and, on the other hand, we consider a data layer associated with the network. Following a similar reasoning, we are able to extend the APA model to spatial networks with different layers. The aim of this paper is to propose a centrality measure for biplex networks that extends the adapted PageRank algorithm centrality for spatial networks with data to the PageRank two-layer approach. Finally, we show an example where the ability to analyze data referring to a group of people from different aspects and using different sets of independent data are revealed.},
DOI = {10.3390/sym11020284}
}

@article{10.1093/bioinformatics/bty965,
    author = {Parvandeh, Saeid and McKinney, Brett A},
    title = "{EpistasisRank and EpistasisKatz: interaction network centrality methods that integrate prior knowledge networks}",
    journal = {Bioinformatics},
    volume = {35},
    number = {13},
    pages = {2329-2331},
    year = {2018},
    month = {11},
    abstract = "{An important challenge in gene expression analysis is to improve hub gene selection to enrich for biological relevance or improve classification accuracy for a given phenotype. In order to incorporate phenotypic context into co-expression, we recently developed an epistasis-expression network centrality method that blends the importance of gene–gene interactions (epistasis) and main effects of genes. Further blending of prior knowledge from functional interactions has the potential to enrich for relevant genes and stabilize classification.We develop two new expression-epistasis centrality methods that incorporate interaction prior knowledge. The first extends our SNPrank (EpistasisRank) method by incorporating a gene-wise prior knowledge vector. This prior knowledge vector informs the centrality algorithm of the inclination of a gene to be involved in interactions by incorporating functional interaction information from the Integrative Multi-species Prediction database. The second method extends Katz centrality to expression-epistasis networks (EpistasisKatz), extends the Katz bias to be a gene-wise vector of main effects and extends the Katz attenuation constant prefactor to be a prior-knowledge vector for interactions. Using independent microarray studies of major depressive disorder, we find that including prior knowledge in network centrality feature selection stabilizes the training classification and reduces over-fitting.Methods and examples provided at https://github.com/insilico/Rinbix and https://github.com/insilico/PriorKnowledgeEpistasisRank.Supplementary data are available at Bioinformatics online.}",
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/bty965},
    url = {https://doi.org/10.1093/bioinformatics/bty965},
    eprint = {https://academic.oup.com/bioinformatics/article-pdf/35/13/2329/36613945/bioinformatics\_35\_13\_2329.pdf},
}



