@article{AG-Analysis-Data-Knowledge,
  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={Security Communications Networks},
  year={2019},
  volume={2019},
  pages={2031063:1-2031063:16}
}

@article{AG-Analysis-Explan,
	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}
}

@incollection{jajodia_topological_2005,
	address = {Boston, MA},
	title = {Topological {Analysis} of {Network} {Attack} {Vulnerability}},
	isbn = {978-0-387-24230-9},
	url = {https://doi.org/10.1007/0-387-24230-9{\_}9},
	booktitle = {Managing {Cyber} {Threats}: {Issues}, {Approaches}, and {Challenges}},
	publisher = {Springer US},
	author = {Jajodia, Sushil and Noel, Steven and O'Berry, Brian},
	editor = {Kumar, Vipin and Srivastava, Jaideep and Lazarevic, Aleksandar},
	year = {2005},
	doi = {10.1007/0-387-24230-9{\_}9},
	pages = {247--266}
}

@inproceedings{cao_assessing_2018,
	address = {Cham},
	title = {Assessing {Attack} {Impact} on {Business} {Processes} by {Interconnecting} {Attack} {Graphs} and {Entity} {Dependency} {Graphs}},
	isbn = {978-3-319-95729-6},
	booktitle = {Data and {Applications} {Security} and {Privacy} {XXXII}},
	publisher = {Springer International Publishing},
	author = {Cao, Chen and Yuan, Lun-Pin and Singhal, Anoop and Liu, Peng and Sun, Xiaoyan and Zhu, Sencun},
	editor = {Kerschbaum, Florian and Paraboschi, Stefano},
	year = {2018},
	pages = {330--348},
}

@ARTICLE{8470942,
  author={Husák, Martin and Komárková, Jana and Bou-Harb, Elias and Čeleda, Pavel},
  journal={IEEE Communications Surveys \& Tutorials}, 
  title={{Survey of Attack Projection, Prediction, and Forecasting in Cyber Security}}, 
  year={2019},
  volume={21},
  number={1},
  pages={640-660},
  doi={10.1109/COMST.2018.2871866}
}

@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}}
}

@misc{fincen,
  title={{Financial Crimes Enforcement Network, Title 31 U.S.C. 310}},
  year={2010},
  note={{Available: https://www.govinfo.gov/content/pkg/USCODE-2010-title31/html/USCODE-2010-title31-subtitleI-chap3-subchapI-sec310.htm}},
}

@misc{fdaqsr,
	title = {Quality System Regulations},
	year = {1996},
	note = {Federal Register: Volume 61, Number 195. 1996 [Online]. Available: https://www.fda.gov/science-research/clinical-trials-and-human-subject-protection/quality-system-regulations},
	author = {{Food and Drug Administration}}
}

@misc{nerccip,
	title = {Critical Infrastructure Protection Reliability Standard CIP},
	year = {2020},
	note = {85 FR 8161. 2020 [Online]. Available: https://www.federalregister.gov/documents/2020/02/13/2020-02173/critical-infrastructure-protection-reliability-standard-cip-012-1-cyber-security-communications},
	author = {{Federal Energy Regulatory Commission}}
}


@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},
  note={{Delft University of Technology.}}
}

@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",
}

@phdthesis{noah_ths,
	author = {Schrick, Noah and Hawrylak, Peter},
	title   = "Compliance Graph Generation Techniques and Parallel Computing Implementations Using Message-Passing Interfaces",
	school  = "The University of Tulsa",
	year    = "2022",
	type    = "{MS} thesis",
	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},
}

@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",
}

@misc{alaya2022mathematical,
	title={Mathematical Analysis of a Delayed SEIRDS Epidemics Models: Deterministic and Stochastic Approach}, 
	author={Mohamed Ben Alaya and Walid Ben Aribi and Slimane Ben Miled},
	year={2022},
	note={arXiv:2208.07690},
	archivePrefix={arXiv},
	primaryClass={q-bio.PE}
}

@article{NetworkX,
	title = {Exploring Network Structure, Dynamics, and Function Using NetworkX},
	author = {Hagberg, Aric and Swart, Pieter J. and Schult, Daniel A.},
	doi = {},
	note = {{Available: https://www.osti.gov/biblio/960616}}, 
	journal = {},
	number = ,
	volume = ,
	place = {United States},
	year = {2008},
	month = {Jan}
}

@Manual{igraph,
    title = {{Igraph}: Network Analysis and Visualization in R},
    author = {Gábor Csárdi and Tamás Nepusz and Vincent Traag and
      Szabolcs Horvát and Fabio Zanini and Daniel Noom and Kirill
      Müller},
    year = {2023},
    note = {R package version 1.5.1},
    doi = {10.5281/zenodo.7682609},
    url = {https://CRAN.R-project.org/package=igraph},
}

@Manual{Graphviz,
	title = {{Graphviz}: Graph Visualization},
	author = {The Graphviz Authors},
	year = {2023},
	note = {Graphviz release 8.1.0 [Online]. Available:https://www.graphviz.org/},
	doi = {10.1007/3-540-45848-4{\_}57},
	url = {https://www.graphviz.org/}
}

@Manual{diffeqjl,
	title = {{DifferentialEquations.jl: Efficient Differential Equation Solving in Julia}},
	author = {{Julia Programming Language}},
	year = {2023},
	note = {{DiffEq.jl v7.7.1}},
	url = {https://docs.sciml.ai/DiffEqDocs/latest/}
}

@mastersthesis{RAGE,
	title = {{RAGE}: {The} {Rage} {Attack} {Graph} {Engine}},
	author = {Cook, Kyle},
	school = {The {University} of {Tulsa}},
	year = {2018},
	file = {Kyle Cook Thesis:/home/noah/Zotero/storage/2SR28HM2/Kyle Cook Thesis.pdf:application/pdf},
}

@Manual{pracma,
    title = {Pracma: Practical Numerical Math Functions},
    author = {Hans W. Borchers},
    year = {2022},
    note = {R package version 2.4.2},
    url = {https://CRAN.R-project.org/package=pracma},
}

@Manual{reticulate,
    title = {Reticulate: Interface to 'Python'},
    author = {Kevin Ushey and JJ Allaire and Yuan Tang},
    year = {2023},
    note = {{R package version 1.28. Available: https://CRAN.R-project.org/package=reticulate}}
}

@thesis{Kalavri2016PerformanceOT,
  title={Performance Optimization Techniques and Tools for Distributed Graph Processing},
  author={Vasiliki Kalavri},
  school={{KTH Royal Institute of Technology, Sweden, and Université Catholique de Louvain, Belgium}},
  year={2016},
  type={{PhD}},
  url={https://api.semanticscholar.org/CorpusID:63506793}
}

@article{10.14778/2947618.2947623,
	author = {Kalavri, Vasiliki and Simas, Tiago and Logothetis, Dionysios},
	title = {The Shortest Path Is Not Always a Straight Line: Leveraging Semi-Metricity in Graph Analysis},
	year = {2016},
	issue_date = {May 2016},
	publisher = {VLDB Endowment},
	volume = {9},
	number = {9},
	issn = {2150-8097},
	url = {https://doi.org/10.14778/2947618.2947623},
	doi = {10.14778/2947618.2947623},
	journal = {Proc. VLDB Endow.},
	month = {may},
	pages = {672-683},
	numpages = {12}
}

@inproceedings{10.1145/3229710.3229730,
	author = {Kim, Jong Wook and Choi, Hyoeun and Bae, Seung-Hee},
	title = {Efficient Parallel All-Pairs Shortest Paths Algorithm for Complex Graph Analysis},
	year = {2018},
	isbn = {9781450365239},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	url = {https://doi.org/10.1145/3229710.3229730},
	doi = {10.1145/3229710.3229730},
	booktitle = {Workshop Proceedings of the 47th International Conference on Parallel Processing},
	articleno = {5},
	numpages = {10},
	keywords = {All-pairs shortest paths, Parallel Algorithms, Shared-memory parallelism},
	location = {Eugene, OR, USA},
	series = {ICPP Workshops '18}
}

@article{Jha2002TwoFA,
  title={Two Formal Analyses of Attack Graphs},
  author={Somesh Jha and Oleg Sheyner and Jeannette M. Wing},
  journal={Proceedings 15th IEEE Computer Security Foundations Workshop. CSFW-15},
  year={2002},
  pages={49-63},
  url={https://api.semanticscholar.org/CorpusID:16108364}
}

@article{Islam2008AHA36,
  title={A Heuristic Approach to Minimum-Cost Network Hardening Using Attack Graph},
  author={Tania Islam and Lingyu Wang},
  journal={2008 New Technologies, Mobility and Security},
  year={2008},
  pages={1-5},
  url={https://api.semanticscholar.org/CorpusID:6625112}
}

@article{10.1016/j.comcom.2006.06.01837,
	author = {Wang, Lingyu and Noel, Steven and Jajodia, Sushil},
	title = {Minimum-Cost Network Hardening Using Attack Graphs},
	year = {2006},
	issue_date = {November, 2006},
	publisher = {Elsevier Science Publishers B. V.},
	address = {NLD},
	volume = {29},
	number = {18},
	issn = {0140-3664},
	url = {https://doi.org/10.1016/j.comcom.2006.06.018},
	doi = {10.1016/j.comcom.2006.06.018},
	journal = {Computer Communications},
	month = {nov},
	pages = {3812-3824},
	numpages = {13},
	keywords = {Intrusion detection, Vulnerability analysis, Intrusion prevention}
}

@inproceedings{10.1109/IAS.2008.38,
	author = {Chen, Feng and Wang, Lingyu and Su, Jinshu},
	title = {An Efficient Approach to Minimum-Cost Network Hardening Using Attack Graphs},
	year = {2008},
	isbn = {9780769533247},
	publisher = {IEEE Computer Society},
	address = {USA},
	url = {https://doi.org/10.1109/IAS.2008.38},
	doi = {10.1109/IAS.2008.38},
	abstract = {Attack graphs can reveal the threat of sophisticated multi-step attacks by enumerating possible sequences of exploits leading to the compromise of given critical resources. Finding a solution to remove such threats by hands is tedious and error prone, particularly for larger and poorly secured networks. Existing automated approaches for hardening a network has an exponential complexity and is not scalable to large networks. This paper proposes a novel approach of applying the Reduced Ordered Binary Decision Diagram (ROBDD) method to network hardening. Existing mature optimization techniques in ROBDD makes the proposed approach an efficient solution that can potentially be applied to large networks.},
	booktitle = {Proceedings of the 2008 The Fourth International Conference on Information Assurance and Security},
	pages = {209-212},
	numpages = {4},
	keywords = {Attack Graphs, Minimum-Cost, network securty, vulnerability},
	series = {IAS '08}
}

@article{JUNCHUN20113227,
	title = {A Minimum Cost of Network Hardening Model Based on Attack Graphs},
	journal = {Procedia Engineering},
	volume = {15},
	pages = {3227-3233},
	year = {2011},
	note = {CEIS 2011},
	issn = {1877-7058},
	doi = {https://doi.org/10.1016/j.proeng.2011.08.606},
	url = {https://www.sciencedirect.com/science/article/pii/S1877705811021072},
	author = {MA Jun-chun and WANG Yong-jun and SUN Ji-yin and CHEN Shan},
	keywords = {network security, attack graphs, bidirectional-based search, genetic algorithms, minimum-cost},
}

@article{0.1117/12.60424,
	author = {Liu, Yu and Man, Hong},
	year = {2005},
	month = {03},
	pages = {},
	title = {Network Vulnerability Assessment Using Bayesian Networks},
	journal = {Proc SPIE},
	doi = {10.1117/12.604240}
}

@inproceedings{10.1145/1456362.1456368,
	author = {Frigault, Marcel and Wang, Lingyu and Singhal, Anoop and Jajodia, Sushil},
	year = {2008},
	month = {10},
	pages = {23-30},
	title = {Measuring Network Security Using Dynamic Bayesian Network},
	doi = {10.1145/1456362.1456368}
}

@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},
	journal = {ACM Trans. Priv. Secur.},
	month = {Jul},
	articleno = {10},
	numpages = {30},
	keywords = {Bayesian networks, approximate inference, probabilistic graphical models}
}

@article{10.1155/2017/3407642,
	author = {{Hu, Hao and Zhang, Hongqi and Liu, Yuling and Wang, Yongwei and Du, Xiaojiang}},
	title = {{Quantitative Method for Network Security Situation Based on Attack Prediction}},
	year = {2017},
	issue_date = {2017},
	publisher = {{John Wiley \& Sons, Inc.}},
	address = {USA},
	volume = {2017},
	issn = {1939-0114},
	url = {https://doi.org/10.1155/2017/3407642},
	doi = {10.1155/2017/3407642},
	journal = {Security and Communications Networks},
	month = {jan},
	numpages = {19}
}

@article{Abraham2014CyberSA,
  title={Cyber Security Analytics: A Stochastic Model for Security Quantification Using Absorbing Markov Chains},
  author={Subil Abraham and Suku Nair},
  journal={Journal of Communications},
  year={2014},
  url={https://api.semanticscholar.org/CorpusID:8554925}
}

@misc{abraham2015predictive,
	title={A Predictive Framework for Cyber Security Analytics Using Attack Graphs}, 
	author={Subil Abraham and Suku Nair},
	year={2015},
	eprint={1502.01240},
	archivePrefix={arXiv},
	primaryClass={cs.CR}
}

@article{Durkota2019HardeningNA,
  title={Hardening Networks Against Strategic Attackers Using Attack Graph Games},
  author={Karel Durkota and V. Lis{\'y} and Branislav Bosansk{\'y} and Christopher Kiekintveld and Michal Pechoucek},
  journal={Computer Security},
  year={2019},
  volume={87},
  url={https://api.semanticscholar.org/CorpusID:201134692}
}

@article{10.1145/3418897,
	author = {Hu, Zhisheng and Zhu, Minghui and Liu, Peng},
	title = {Adaptive Cyber Defense Against Multi-Stage Attacks Using Learning-Based POMDP},
	year = {2020},
	issue_date = {February 2021},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	volume = {24},
	number = {1},
	issn = {2471-2566},
	url = {https://doi.org/10.1145/3418897},
	doi = {10.1145/3418897},
	journal = {ACM Transactions on Privacy and Security},
	month = {nov},
	articleno = {6},
	numpages = {25},
	keywords = {adaptive cyber defense, Thompson sampling, Reinforcement learning}
}

@article{Miehling2018APA,
  title={A POMDP Approach to the Dynamic Defense of Large-Scale Cyber Networks},
  author={Erik Miehling and Mohammad Reza Rasouli and Demosthenis Teneketzis},
  journal={IEEE Transactions on Information Forensics and Security},
  year={2018},
  volume={13},
  pages={2490-2505},
  url={https://api.semanticscholar.org/CorpusID:13705714}
}

@article{Nguyen2017AnAT,
  title={An Approach to Incorporating Uncertainty in Network Security Analysis},
  author={Hoang Hai Nguyen and Kartik Palani and David M. Nicol},
  journal={Proceedings of the Hot Topics in Science of Security: Symposium and Bootcamp},
  year={2017},
  url={https://api.semanticscholar.org/CorpusID:16666067}
}

@Inbook{Wang2017,
	author="Wang, Lingyu
	and Jajodia, Sushil
	and Singhal, Anoop
	and Cheng, Pengsu
	and Noel, Steven",
	title="k-Zero Day Safety: Evaluating the Resilience of Networks Against Unknown Attacks",
	bookTitle="Network Security Metrics",
	year="2017",
	publisher="Springer International Publishing",
	address="Cham",
	pages="75--93",
	isbn="978-3-319-66505-4",
	doi="10.1007/978-3-319-66505-4{\_}4",
	url="https://doi.org/10.1007/978-3-319-66505-4{\_}4"
}

@InProceedings{10.1007/978-3-030-64793-3_24,
	author="Anwar, Ahmed H.
	and Kamhoua, Charles",
	editor="Zhu, Quanyan
	and Baras, John S.
	and Poovendran, Radha
	and Chen, Juntao",
	title="Game Theory on Attack Graph for Cyber Deception",
	booktitle="Decision and Game Theory for Security",
	year="2020",
	publisher="Springer International Publishing",
	address="Cham",
	pages="445--456",
	abstract="Game Theory provides a set of tools and a framework suitable to study security problems. In this paper, a class of games is developed to study cyber deception and the interactions between the network defender who is deceiving an adversary to mitigate the damage of the attack. In order to capture network topology, each game is played over an attack graph that can be generated according to the vulnerabilities associated with each node. The defender's goal is to take deceptive actions to prevent the attacker from taking control over the network resources exploiting the incomplete information of the attacker regarding the deceptive network gained through the attack reconnaissance stage. To this end, we present several games such as normal form static, dynamic, hypergame, and a partially observable stochastic game (POSG) to study the game dynamics at different information structures. For the most general class of games, (i.e., POSG), we provide multiple solution approaches to overcome the intractability of the game model and finally present numerical result samples to show the effectiveness of each solution approach.",
	isbn="978-3-030-64793-3"
}

@INBOOK{9124037,
  author={Xi, Bowei and Kamhoua, Charles A.},
  booktitle={Modeling and Design of Secure Internet of Things}, 
  title={A Hypergame-Based Defense Strategy Toward Cyber Deception in Internet of Battlefield Things (IoBT)}, 
  year={2020},
  volume={},
  number={},
  pages={59-77},
  doi={10.1002/9781119593386.ch3}
}

@InProceedings{10.1007/978-3-030-64793-3_9,
	author="Kulkarni, Abhishek N.
	and Fu, Jie
	and Luo, Huan
	and Kamhoua, Charles A.
	and Leslie, Nandi O.",
	editor="Zhu, Quanyan
	and Baras, John S.
	and Poovendran, Radha
	and Chen, Juntao",
	title="Decoy Allocation Games on Graphs with Temporal Logic Objectives",
	booktitle="Decision and Game Theory for Security",
	year="2020",
	publisher="Springer International Publishing",
	address="Cham",
	pages="168--187",
	isbn="978-3-030-64793-3"
}

@conference{1503283,
	title = {Security Scheduling for Real-World Networks },
	booktitle = {International Conference on Autonomous Agents and Multiagent Systems (AAMAS)},
	year = {2013},
	author = {Jain, Manish and Vincent Conitzer and Tambe, Milind}
}

@article{HORAK2019101579,
	title = {Optimizing Honeypot Strategies Against Dynamic Lateral Movement Using Partially Observable Stochastic Games},
	journal = {Computers \& Security},
	volume = {87},
	pages = {101579},
	year = {2019},
	issn = {0167-4048},
	doi = {https://doi.org/10.1016/j.cose.2019.101579},
	url = {https://www.sciencedirect.com/science/article/pii/S0167404819300665},
	author = {Karel Horák and Branislav Bošanský and Petr Tomášek and Christopher Kiekintveld and Charles Kamhoua},
	keywords = {Dynamic honeypot allocation, Lateral movement, Partially observable stochastic games, Compact representation, Incremental strategy generation}
}

@article{10.1080/23311916.2018.1502242,
	author = {Gunantara, Nyoman},
	year = {2018},
	month = {07},
	pages = {},
	title = {A Review of Multi-Objective Optimization: Methods and Its Applications},
	volume = {5},
	journal = {Cogent Engineering},
	doi = {10.1080/23311916.2018.1502242}
}

@Inbook{Awange2023,
	author="Awange, Joseph L.
	and Pal{\'a}ncz, B{\'e}la
	and Lewis, Robert H.
	and V{\"o}lgyesi, Lajos",
	title="Multiobjective Optimization",
	bookTitle="Mathematical Geosciences: Hybrid Symbolic-Numeric Methods",
	year="2023",
	publisher="Springer International Publishing",
	address="Cham",
	pages="319--352",
	isbn="978-3-030-92495-9",
	doi="10.1007/978-3-030-92495-9{\_}9",
	url="https://doi.org/10.1007/978-3-030-92495-9{\_}9"
}

@article{MIRJALILI2015228,
	title = {Moth-Flame Optimization Algorithm: A Novel Nature-Inspired Heuristic Paradigm},
	journal = {Knowledge-Based Systems},
	volume = {89},
	pages = {228-249},
	year = {2015},
	issn = {0950-7051},
	doi = {https://doi.org/10.1016/j.knosys.2015.07.006},
	url = {https://www.sciencedirect.com/science/article/pii/S0950705115002580},
	author = {Seyedali Mirjalili},
	keywords = {Optimization, Stochastic optimization, Constrained optimization, Meta-heuristic, Population-based algorithm}
}

@INPROCEEDINGS{7732428,
  author={Vikas and Nanda, Satyasai Jagannath},
  booktitle={2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI)}, 
  title={Multi-Objective Moth Flame Optimization}, 
  year={2016},
  volume={},
  number={},
  pages={2470-2476},
  doi={10.1109/ICACCI.2016.7732428}
}

@INPROCEEDINGS{9563556,
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  booktitle={2021 IEEE Madras Section Conference (MASCON)}, 
  title={Development of Hybrid Algorithm Using Moth-Flame and Particle Swarm Optimization}, 
  year={2021},
  volume={},
  number={},
  pages={1-6},
  doi={10.1109/MASCON51689.2021.9563556}
}

@article{Sharifi2021ANO,
  title={A New Optimization Algorithm to Solve Multi-Objective Problems},
  author={Mohammad Reza Sharifi and Saeid Akbarifard and Kourosh Qaderi and Mohamad Reza Madadi},
  journal={Scientific Reports},
  year={2021},
  volume={11},
  url={https://api.semanticscholar.org/CorpusID:238860219}
}

@book{10.1007/978-3-540-24777-7,
	author = {Kellerer, Hans and Pferschy, Ulrich and Pisinger, David},
	year = {2004},
	month = {Jan.},
	pages = {},
	title = {Knapsack Problems},
	isbn = {978-3-540-40286-2},
	journal = {Knapsack Problems},
	doi = {10.1007/978-3-540-24777-7},
	publisher      = {Springer Berlin, Heidelberg},
}

@INPROCEEDINGS{8204118,
  author={Gupta, Indresh Kumar and Choubey, Abha and Choubey, Siddhartha},
  booktitle={2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT)}, 
  title={Clustered Genetic Algorithm to Solve Multidimensional Knapsack Problem}, 
  year={2017},
  volume={},
  number={},
  pages={1-6},
  doi={10.1109/ICCCNT.2017.8204118}
}

@ARTICLE{9142411,
  author={Li, Zuocheng and Tang, Lixin and Liu, Jiyin},
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  title={A Memetic Algorithm Based on Probability Learning for Solving the Multidimensional Knapsack Problem}, 
  year={2022},
  volume={52},
  number={4},
  pages={2284-2299},
  doi={10.1109/TCYB.2020.3002495}
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@INPROCEEDINGS{9308380,
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  booktitle={2020 IEEE Symposium Series on Computational Intelligence (SSCI)}, 
  title={Optimal Lagrangian Multipliers for the Multidimensional Knapsack Problem: A Bayesian Optimisation Approach}, 
  year={2020},
  volume={},
  number={},
  pages={3149-3155},
  doi={10.1109/SSCI47803.2020.9308380}
}

@INPROCEEDINGS{5455187,
  author={Shan, Bowei},
  booktitle={2009 First International Conference on Information Science and Engineering}, 
  title={The Spread of Malware on the WiFi Network: Epidemiology Model and Behaviour Evaluation}, 
  year={2009},
  volume={},
  number={},
  pages={1916-1918},
  doi={10.1109/ICISE.2009.1285}
}

@INPROCEEDINGS{8228672,
  author={Mitchell, Robert},
  booktitle={2017 IEEE Conference on Communications and Network Security (CNS)}, 
  title={Epidemic-Resistant Configurations for Intrusion Detection Systems}, 
  year={2017},
  volume={},
  number={},
  pages={487-494},
  doi={10.1109/CNS.2017.8228672}
}

@ARTICLE{6414589,
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@INPROCEEDINGS{9450250,
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  booktitle={2020 IEEE 19th International Conference on Cognitive Informatics \& Cognitive Computing (ICCI*CC)}, 
  title={The Cognitive and Mathematical Foundations of Analytic Epidemiology}, 
  year={2020},
  volume={},
  number={},
  pages={6-14},
  doi={10.1109/ICCICC50026.2020.9450250}
}

@INPROCEEDINGS{9457692,
  author={Parwez, Md. Aslam and Abulaish, Muhammad and Jahiruddin, Jahiruddin},
  booktitle={2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)}, 
  title={A Social Media Time-Series Data Analytics Approach for Digital Epidemiology}, 
  year={2020},
  volume={},
  number={},
  pages={852-859},
  doi={10.1109/WIIAT50758.2020.00131}
}

@misc{j_hale_compliance_nodate,
	title = {Compliance {Method} for a {Cyber}-{Physical} {System}},
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	file = {Complaince{\_}Graph{\_}US{\_}Patent{\_}9471789:/home/noah/Zotero/storage/55BZN4U7/Complaince{\_}Graph{\_}US{\_}Patent{\_}9471789.pdf:application/pdf},
}

@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},
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	author = {Baloyi, Ntsako and Kotzé, Paula},
	year = {2019},
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@article{allman_complying_2006,
	title = {Complying With {Compliance}: {Blowing} It Off Is Not an Option.},
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	author = {Allman, Eric},
	year = {2006},
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@ARTICLE{9914620,
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  journal={IEEE Journal of Biomedical and Health Informatics}, 
  title={Reproduction Factor Based Latent Epidemic Model Inference: A Data-Driven Approach Using COVID-19 Datasets}, 
  year={2023},
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}

@INPROCEEDINGS{9929470,
  author={Roy, Tamal Joyti and Mahmood, Md. Ashiq and Mohanta, Aninda and Roy, Diti},
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  title={An Analytical Approach to Predict the COVID-19 Death Rate in Bangladesh Utilizing Multiple Regression and SEIR Model}, 
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  volume={},
  number={},
  pages={42-45},
  doi={10.1109/RAAICON54709.2021.9929470}
}

@INPROCEEDINGS{9628991,
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  booktitle={2021 IEEE 4th International Conference on Advanced Information and Communication Technologies (AICT)}, 
  title={Simulation of COVID-19 Dynamics Using Ridge Regression}, 
  year={2021},
  volume={},
  number={},
  pages={163-166},
  doi={10.1109/AICT52120.2021.9628991}
}

@INPROCEEDINGS{9630798,
  author={Zhang, Siqi and Yang, Hui},
  booktitle={2021 43rd Annual International Conference of the IEEE Engineering in Medicine \& Biology Society (EMBC)}, 
  title={Spatial Modeling and Analysis of Human Traffic and Infectious Virus Spread in Community Networks}, 
  year={2021},
  volume={},
  number={},
  pages={2286-2289},
  doi={10.1109/EMBC46164.2021.9630798}
}

@INPROCEEDINGS{9678822,
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  booktitle={2021 IEEE 3rd International Conference on Advanced Trends in Information Theory (ATIT)}, 
  title={Dynamic Model of the Spread of Viruses in a Computer Network Using Differential Equations}, 
  year={2021},
  volume={},
  number={},
  pages={111-115},
  doi={10.1109/ATIT54053.2021.9678822}
}

@INPROCEEDINGS{10227113,
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  booktitle={2022 IEEE International Symposium on Technology and Society (ISTAS)}, 
  title={Investigating Relationship Conflict Within the Social Network of Large IS Projects Using a SIR Model}, 
  year={2022},
  volume={1},
  number={},
  pages={1-5},
  doi={10.1109/ISTAS55053.2022.10227113}
}

@INPROCEEDINGS{9856356,
  author={Mathebula, Dephney},
  booktitle={2022 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD)}, 
  title={Novel Data-Based Model for Future Epidemiology}, 
  year={2022},
  volume={},
  number={},
  pages={1-6},
  doi={10.1109/icABCD54961.2022.9856356}
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@INPROCEEDINGS{9593147,
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  booktitle={2021 International Balkan Conference on Communications and Networking (BalkanCom)}, 
  title={Dynamics of Multi-Strain Malware Epidemics Over Duty-Cycled Wireless Sensor Networks}, 
  year={2021},
  volume={},
  number={},
  pages={1-5},
  doi={10.1109/BalkanCom53780.2021.9593147}
}

@Article{Lou2021,
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	title={Control Strategies for a Multi-Strain Epidemic Model},
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	year={2021},
	month={Nov},
	day={27},
	volume={84},
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}

@article{10.1371/journal.pone.0257512,
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@inproceedings{GCAI-2018:Analysis_of_Attack_Graph,
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  url       = {https://easychair.org/publications/paper/ZBHj},
  doi       = {10.29007/2c1q}
}

@article{10.1371/journal.pone.0053095,
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    publisher = {Public Library of Science},
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    year = {2013},
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}

@inproceedings{10.1145/3288599.3295597,
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	title = {A Percolation-Based Recovery Mechanism for Bot Infected P2P Cloud},
	year = {2019},
	isbn = {9781450360944},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	url = {https://doi-org.utulsa.idm.oclc.org/10.1145/3288599.3295597},
	doi = {10.1145/3288599.3295597},
	abstract = {Execution of any recovery procedure in compromised nodes of a Cloud should aim at covering as many nodes as possible in a given time. The authors propose an innovative approach using the concept of percolation centrality to spread the execution of a recovery procedure across adjacent nodes in a P2PCloud. Compromised networks can be large since botnets and malware infections use the advantage of the internet to spread rapidly. Using percolation centrality to choose a node from where to begin, the recovery procedure runs in parallel in different nodes which can be reached from a chosen node and spreads fast.},
	booktitle = {Proceedings of the 20th International Conference on Distributed Computing and Networking},
	pages = {474-479},
	numpages = {6},
	keywords = {churning, botnet, P2PCloud, P2P network, percolation centrality},
	location = {Bangalore, India},
	series = {ICDCN '19}
}

@INPROCEEDINGS{9680376,
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  title={Efficient Parallel Algorithms for Computing Percolation Centrality}, 
  year={2021},
  volume={},
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  pages={111-120},
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}

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}

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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},
}

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}

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}

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      primaryClass={cs.IT}
}

@misc{seifert2023causal,
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      note={arXiv:2209.07970},
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}

@BOOK{laug,
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                Du Croz, J. and Greenbaum, A. and Hammarling, S. and
                McKenney, A. and Sorensen, D.},
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      PUBLISHER = {Society for Industrial and Applied Mathematics},
      YEAR = {1999},
      ADDRESS = {Philadelphia, PA},
      ISBN = {0-89871-447-8 (paperback)} }

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author = {Hugo Daniel Macedo},
}

@misc{williams2023new,
      title={New Bounds for Matrix Multiplication: From Alpha to Omega}, 
      author={Virginia Vassilevska Williams and Yinzhan Xu and Zixuan Xu and Renfei Zhou},
      year={2023},
      eprint={2307.07970},
      archivePrefix={arXiv},
      primaryClass={cs.DS}
}

@misc{alman2020refined,
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}
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}

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	title = {{Standard} 1910 {Subpart} {H} {Hazardous} {Materials}},
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}


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@ARTICLE{10124989,
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  keywords={Firing;Explosions;Automobiles;Computer security;Flexible printed circuits;Cyber-physical systems;Regulation;Attack graph;compliance and regulation;compliance graph;cybersecurity;high-performance computing;speedup;synchronous firing},
  doi={10.1109/OJCS.2023.3276370}}


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pages = {486-490},
doi = {https://doi.org/10.1112/plms/s1-28.1.486},
url = {https://londmathsoc.onlinelibrary.wiley.com/doi/abs/10.1112/plms/s1-28.1.486},
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year = {1896}
}

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number = {2},
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author = {Alberto Caprara and Hans Kellerer and Ulrich Pferschy and David Pisinger}
}

@article{Jangir2018NonDominatedSM,
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}

@manual{Octave,
	title     = {{GNU Octave} Version 8.4.0 Manual: A High-Level Interactive Language for Numerical Computations},
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	year      = {2023},
	note       = {{[Online]. Available: https://www.gnu.org/software/octave/doc/v8.4.0/}}
}

@misc{gplv3,
  title        = {{GNU General Public License}},
  version      = {3},
  shorthand    = {GPL},
  organization = {Free Software Foundation},
  note          = {{[Online]. Available: http://www.gnu.org/licenses/gpl.html}},
  pagination   = {section},
  language     = {english},
  date         = {2007-06-29}
}

@article{10.1162/106365600568202,
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    number = {2},
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    year = {2000},
    month = {06},
    abstract = "{In this paper, we provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions. Each test function involves a particular feature that is known to cause difficulty in the evolutionary optimization process, mainly in converging to the Pareto-optimal front (e.g., multimodality and deception). By investigating these different problem features separately, it is possible to predict the kind of problems to which a certain technique is or is not well suited. However, in contrast to what was suspected beforehand, the experimental results indicate a hierarchy of the algorithms under consideration. Furthermore, the emerging effects are evidence that the suggested test functions provide sufficient complexity to compare multiobjective optimizers. Finally, elitism is shown to be an important factor for improving evolutionary multiobjective search.}",
    issn = {1063-6560},
    doi = {10.1162/106365600568202},
    url = {https://doi.org/10.1162/106365600568202},
    eprint = {https://direct.mit.edu/evco/article-pdf/8/2/173/1493199/106365600568202.pdf},
}

@article{doi:10.1287/mnsc.27.1.1,
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}

@article{3c419982-0884-3763-8914-983157eab6e5,
	ISSN = {0030364X, 15265463},
	URL = {http://www.jstor.org/stable/168028},
	abstract = {The usefulness of Lagrange multipliers for optimization in the presence of constraints is not limited to differentiable functions. They can be applied to problems of maximizing an arbitrary real valued objective function over any set whatever, subject to bounds on the values of any other finite collection of real valued functions defined on the same set. While the use of the Lagrange multipliers does not guarantee that a solution will necessarily be found for all problems, it is 'fail-safe' in the sense that any solution found by their use is a true solution. Since the method is so simple compared to other available methods it is often worth trying first, and succeeds in a surprising fraction of cases. They are particularly well suited to the solution of problems of allocating limited resources among a set of independent activities.},
	author = {Hugh Everett},
	journal = {Operations Research},
	number = {3},
	pages = {399--417},
	publisher = {INFORMS},
	title = {Generalized Lagrange Multiplier Method for Solving Problems of Optimum Allocation of Resources},
	urldate = {2024-03-13},
	volume = {11},
	year = {1963}
}

@inproceedings{Nocedal2018NumericalO,
  title={Numerical Optimization},
  author={Jorge Nocedal and Stephen J. Wright},
  booktitle={Fundamental Statistical Inference},
  year={2018},
  url={https://api.semanticscholar.org/CorpusID:189864167}
}

@Manual{pracma,
    title = {Pracma: Practical Numerical Math Functions},
    author = {Hans W. Borchers},
    year = {2023},
    note = {{R package version 2.4.4 [Online]. Available: https://CRAN.R-project.org/package=pracma}}
  }

@article{c8bc284a-942a-3e27-9ea0-6db64782cbb2,
 ISSN = {00129682, 14680262},
 URL = {http://www.jstor.org/stable/1909582},
 abstract = {Several models for limited dependent variables (variables having a non-negligible probability of exactly equaling zero) are examined. Estimation in and discrimination among the various models are considered, followed by a small sampling experiment into the procedures and an example of their application.},
 author = {John G. Cragg},
 journal = {Econometrica},
 number = {5},
 pages = {829--844},
 publisher = {[Wiley, Econometric Society]},
 title = {Some Statistical Models for Limited Dependent Variables With Application to the Demand for Durable Goods},
 urldate = {2024-03-27},
 volume = {39},
 year = {1971}
}

@article{fc317238-6f24-34d1-86ea-e81c1292d7e9,
 ISSN = {00401706},
 URL = {http://www.jstor.org/stable/1269547},
 abstract = {Zero-inflated Poisson (ZIP) regression is a model for count data with excess zeros. It assumes that with probability p the only possible observation is 0, and with probability 1 - p, a Poisson(λ) random variable is observed. For example, when manufacturing equipment is properly aligned, defects may be nearly impossible. But when it is misaligned, defects may occur according to a Poisson(λ) distribution. Both the probability p of the perfect, zero defect state and the mean number of defects λ in the imperfect state may depend on covariates. Sometimes p and λ are unrelated; other times p is a simple function of λ such as p=1/(1+λ τ) for an unknown constant τ. In either case, ZIP regression models are easy to fit. The maximum likelihood estimates (MLE's) are approximately normal in large samples, and confidence intervals can be constructed by inverting likelihood ratio tests or using the approximate normality of the MLE's. Simulations suggest that the confidence intervals based on likelihood ratio tests are better, however. Finally, ZIP regression models are not only easy to interpret, but they can also lead to more refined data analyses. For example, in an experiment concerning soldering defects on printed wiring boards, two sets of conditions gave about the same mean number of defects, but the perfect state was more likely under one set of conditions and the mean number of defects in the imperfect state was smaller under the other set of conditions; that is, ZIP regression can show not only which conditions give lower mean number of defects but also why the means are lower.},
 author = {Diane Lambert},
 journal = {Technometrics},
 number = {1},
 pages = {1--14},
 publisher = {[Taylor & Francis, Ltd., American Statistical Association, American Society for Quality]},
 title = {Zero-Inflated Poisson Regression, With an Application to Defects in Manufacturing},
 urldate = {2024-03-27},
 volume = {34},
 year = {1992}
}


