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Livres sur le sujet « Analysis of encrypted network flow »

Auteur : Grafiati

Publié le 16 novembre 2024

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1

Cherukuri, Aswani Kumar, Sumaiya Thaseen Ikram, Gang Li et Xiao Liu. Encrypted Network Traffic Analysis. Cham : Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-62909-9.

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2

Jensen, Paul A. Network flow programming. Malabar, Fla : R.E. Krieger Pub. Co., 1987.

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3

Williams-Sether, Tara. Analysis of the peak-flow gaging network in North Dakota. Bismarck, N.D : U.S. Dept. of the Interior, U.S. Geological Survey, 1996.

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4

Williams-Sether, Tara. Analysis of the peak-flow gaging network in North Dakota. Bismarck, N.D : U.S. Dept. of the Interior, U.S. Geological Survey, 1996.

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5

Williams-Sether, Tara. Analysis of the peak-flow gaging network in North Dakota. Bismarck, N.D : U.S. Dept. of the Interior, U.S. Geological Survey, 1996.

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6

Tara, Williams-Sether. Analysis of the peak-flow gaging network in North Dakota. Bismarck, N.D : U.S. Dept. of the Interior, U.S. Geological Survey, 1996.

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7

Tara, Williams-Sether. Analysis of the peak-flow gaging network in North Dakota. Bismarck, N.D : U.S. Dept. of the Interior, U.S. Geological Survey, 1996.

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8

Williams-Sether, Tara. Analysis of the peak-flow gaging network in North Dakota. Bismarck, N.D : U.S. Dept. of the Interior, U.S. Geological Survey, 1996.

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9

Tara, Williams-Sether. Analysis of the peak-flow gaging network in North Dakota. Bismarck, N.D : U.S. Dept. of the Interior, U.S. Geological Survey, 1996.

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10

Tara, Williams-Sether. Analysis of the peak-flow gaging network in North Dakota. Bismarck, N.D : U.S. Dept. of the Interior, U.S. Geological Survey, 1996.

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11

Goldberg, Andrew V. A natural randomization strategy for multicommodity flow and related algorithms. Stanford, Calif : Dept. of Computer Science, Stanford University, 1991.

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12

T, Leighton, dir. Fast approximation algorithms for multicommodity flow problems. Stanford, Calif : Dept. of Computer Science, Stanford University, 1991.

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13

Jung, Soojung. Roadway network productivity assessment : System-wide analysis under variant travel demand. Washington, D.C : U.S. Dept. of Transportation, Federal Highway Administration, 2009.

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14

Janetzke, David C. Efficient computation of aerodynamic influence coefficients for aeroelastic analysis on a transputer network. [Washington, DC] : National Aeronautics and Space Administration, 1991.

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15

Antoni, Mazurkiewicz, MONDILEX et Instytut Slawistyki (Polska Akademia Nauk), dir. Time flow and tenses. Warsaw : Slawistyczny Ośrodek Wydawniczy, 2010.

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16

Aikens, C. Harold. A fixed-charge multicommodity network flow algorithm and a warehouse location application. Monterey, Calif : Naval Postgradute School, 1985.

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17

Slatnick, Jennifer A. Network flow model analysis of the impact of chlorofluorocarbon phaseout on acid-grade fluorspar. Washington, DC : U.S. Dept. of the Interior, Bureau of Mines, 1994.

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18

Derigs, Ulrich. Programming in networks and graphs : On the combinatorial background and near-equivalence of network flow and matching algorithms. Berlin : Springer-Verlag, 1988.

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19

Boyle, Edward H. Chromium availability in market economy countries and network flow model analysis of world chromium supply. [PGH, PA] : U.S. Dept. of the Interior, Bureau of Mines, 1993.

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20

Omang, R. J. Analysis of the magnitude and frequency of floods and the peak-flow gaging network in Montana. Helena, Mont : U.S. Dept. of the Interior, U.S. Geological Survey, 1992.

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21

Brandimarte, Paolo. Introduction to distribution logistics. Hoboken, N.J : John Wiley, 2007.

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22

Spirin, Yuriy, Sergey Zotov, Evgeniy Krasnov et Nadezhda Cvetkova. Polder watercourses : research methods and geoecological assessment. ru : INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1903343.

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The monograph describes research methods and geoecological assessment of polder lands. The history of research and practical use of surface waters of the Kaliningrad region is characterized, modern problems of their geoecological state are revealed. The characteristic of polder lands as complex natural and economic systems is given, the necessity of geoecological studies of the catchments of small rivers located on them is substantiated. A methodological approach has been developed to assess the geoecological condition of the basins of small watercourses through the combined analysis of hydrological, hydrochemical and geoecological data. Hydrological calculations of the key characteristics of the river flow of small watercourses of polder lands were carried out, the dependencies between them were revealed. Field studies were carried out, hydrochemical data sets were obtained on the rivers of polder lands for four hydrological seasons of 2020-2021. Retrospective hydrochemical information has been processed. The prevailing pollutants, integral indicators of water quality and the main sources of pollution are calculated. Based on the results obtained, a scheme of spatial distribution of pollution of the river network of polder lands is constructed. The geoecological condition of the basins is assessed on the basis of a complex of natural and anthropogenic data; based on the result obtained, recommendations for its improvement are given. It is of interest to specialists in the field of geoecology, hydrology, hydrochemistry, environmental management and environmental protection.

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23

Ikram, Sumaiya Thaseen. Encrypted Network Traffic Analysis. Springer International Publishing AG, 2024.

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24

Network flow analysis. San Francisco : No Starch Press, 2010.

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25

Garcia-Diaz, Alberto, et Don T. Phillips. Fundamentals of Network Analysis and Flow Optimization. Virtualbookworm.com Publishing, Incorporated, 2022.

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26

Masao. Network Flow, Transportation, and Scheduling ; Theory and Algorithms. Elsevier Science & Technology Books, 2011.

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27

Sepil, Canan A. Dual simplex algorithms on network flow problems and extensions. 1987.

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28

Choi, Wonjoon. Network flow models of building evacuation problems with flow- dependent arc capacities. 1987.

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29

Jin, Wenlong. Introduction to Network Traffic Flow Theory : Modeling, Analysis, Simulation, and Empirics. Elsevier, 2020.

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30

Jin, Wenlong. Introduction to Network Traffic Flow Theory : Modeling, Analysis, Simulation, and Empirics. Elsevier, 2021.

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31

Derigs, Ulrich. Programming in Networks and Graphs : On the Combinatorial Background and near-Equivalence of Network Flow and Matching Algorithms. Springer London, Limited, 2013.

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32

Derigs, Ulrich. Programming in Networks and Graphs : On The Combinatorial Background And Near-Equivalence Of Network Flow And Matching Algorithms. Springer, 1988.

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33

Lee, Seung-Chul. A new power flow and sensitivity analysis method using a generalized power flow algorithm, interactive computer graphics, and artificial intelligence. 1985.

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34

Zotteri, Giulio, et Paolo Brandimarte. Introduction to Distribution Logistics. Wiley & Sons, Incorporated, John, 2007.

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35

Zotteri, Giulio, et Paolo Brandimarte. Introduction to Distribution Logistics. Wiley & Sons, Incorporated, John, 2007.

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36

Light, Ryan, et James Moody, dir. The Oxford Handbook of Social Networks. Oxford University Press, 2020. http://dx.doi.org/10.1093/oxfordhb/9780190251765.001.0001.

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Social networks fundamentally shape our lives. Networks channel the ways that information, emotions, and diseases flow through populations. Networks reflect differences in power and status in settings ranging from small peer groups to international relations across the globe. Network tools even provide insights into the ways that concepts, ideas and other socially generated contents shape culture and meaning. As such, the rich and diverse field of social network analysis has emerged as a central tool across the social sciences. This Handbook provides an overview of the theory, methods, and substantive contributions of this field. The thirty-three chapters move through the basics of social network analysis aimed at those seeking an introduction to advanced and novel approaches to modeling social networks statistically. The Handbook includes chapters on data collection and visualization, theoretical innovations, links between networks and computational social science, and how social network analysis has contributed substantively across numerous fields. As networks are everywhere in social life, the field is inherently interdisciplinary and this Handbook includes contributions from leading scholars in sociology, archaeology, economics, statistics, and information science among others.

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37

Woolley, Samuel C., et Douglas Guilbeault. United States. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190931407.003.0009.

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Do bots have the capacity to influence the flow of political information over social media? This chapter answers this question through two methodological avenues: a) a qualitative analysis of how political bots were used to support United States presidential candidates and campaigns during the 2016 election, and b) a network analysis of bot influence on Twitter during the same event. Political bots are automated software programs that operate on social media, written to mimic real people in order to manipulate public opinion. The qualitative findings are based upon nine months of fieldwork on the campaign trail, including interviews with bot makers, digital campaign strategists, security consultants, campaign staff, and party officials. During the 2016 campaign, a bipartisan range of domestic and international political actors made use of political bots. The Republican Party, including both self-proclaimed members of the “alt-right” and mainstream members, made particular use of these digital political tools throughout the election. Meanwhile, public conversation from campaigners and government representatives is inconsistent about the political influence of bots. This chapter provides ethnographic evidence that bots affect information flows in two key ways: 1) by “manufacturing consensus,” or giving the illusion of significant online popularity in order to build real political support, and 2) by democratizing propaganda through enabling nearly anyone to amplify online interactions for partisan ends. We supplement these findings with a quantitative network analysis of the influence bots achieved within retweet networks of over 4 million tweets, collected during the 2016 US election. The results of this analysis confirm that bots reached positions of measurable influence during the 2016 US election.

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38

Brughmans, Tom, Anna Collar et Fiona Coward, dir. The Connected Past. Oxford University Press, 2016. http://dx.doi.org/10.1093/oso/9780198748519.001.0001.

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One of the most exciting recent developments in archaeology and history has been the adoption of new perspectives which see human societies in the past--as in the present--as made up of networks of interlinked individuals. This view of people as always connected through physical and conceptual networks along which resources, information, and disease flow, requires archaeologists and historians to use new methods to understand how these networks form, function, and change over time. The Connected Past provides a constructive methodological and theoretical critique of the growth in research applying network perspectives in archaeology and history and considers the unique challenges presented by datasets in these disciplines, including the fragmentary and material nature of such data and the functioning and change of social processes over long timespans. An international and multidisciplinary range of scholars debate both the rationale and practicalities of applying network methodologies, addressing the merits and drawbacks of specific techniques of analysis for a range of datasets and research questions, and demonstrating their approaches with concrete case studies and detailed illustrations. As well as revealing the valuable contributions archaeologists and historians can make to network science, the volume represents a crucial step towards the development of best practice in the field, especially in exploring the interactions between social and material elements of networks, and long-term network evolution.

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39

Herrnson, Paul S., et Justin H. Kirkland. Political Parties and Campaign Finance Networks. Sous la direction de Jennifer Nicoll Victor, Alexander H. Montgomery et Mark Lubell. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190228217.013.16.

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This chapter argues that to accurately gauge the impact of political parties on elections, it is necessary to look beyond traditional party committees and consider these organizations’ impact on the thousands of other groups that participate in the financing of campaigns. Using social network analysis and federal campaign finance data, the chapter demonstrates the existence of two distinctive hierarchical extended party networks, each comprising formal party organizations, campaign committees associated with members of Congress and congressional candidates, and PACs allied with one party. It also provides evidence suggesting party organizations’ influence over hundreds of millions of dollars in contributions and expenditures that flow within their networks. The chapter’s results have implications for party and interest group development and behavior, as well as legislative and electoral politics.

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40

Perdigão, Rui A. P. Unfolding the Manifold Flavours of Causality. Meteoceanics Institute, 2024. http://dx.doi.org/10.46337/mdsc.1804.

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The present work provides a contribution to an overarching cross-methodological causality investigation, encompassing a methodological synergy among physical, analytical, information-theoretic and systems intelligence approaches to causal discovery and quantification in complex system dynamics. These efforts methodologically lead to the emergence of a broader causal framework, valid not only in classical recurrence-based dynamical systems, but also on the generalized information physics of non-ergodic coevolutionary spatiotemporal complexity. This study begins with a comprehensive cross-examination of causality metrics derived from these diverse domains, by synthesizing causality insights from information theory, which enables the quantification of information flow among variables; differential geometry, which captures the curvature and structure of causal relationships; dynamical systems, which analyze the temporal evolution of systems and associated kinematic geometric properties; and fundamental physical metrics, which elucidate causal connections in the physical world from fundamental thermodynamic principles. Through this analysis, we aim to deepen our understanding of causality in complex systems, with physical process understanding and geophysical applications in mind. Having laid out some of the key methodological flavours of causality, the present communication introduces new metrics further contributing to a broader non-Shannonian information theoretic causality pool of methods, along with additional advances on quantum thermodymamical, nonlinear statistical mechanical, differential geometric and topologic approaches on causality. Positioning ourselves in a broader nonlinear non-Gaussian non-ergodic setting to tackle far-from-equilibrium structural-functional coevolution and synergistic emergence in complex system dynamics, our derivations further contribute to a new generation of information theoretic, dynamical systems and non-equilibrium thermodynamic causality approaches, along with their synergistic articulation towards a unified framework. This brings out further cross-methodological comparability, portability and complementary insights on dealing with the intricate causality of complex multiscale far-from-equilibrium Earth system dynamic phenomena. By unveiling manifold flavours of causality and their interconnections, this study brings out their commonalities, synergies, and further potential synergistic applications across disciplines. This interdisciplinary approach not only enhances our theoretical understanding of causality but also provides practical implications for applications in fields such as data science, network theory, and complex systems analysis, with direct relevance across a plethora of scientific, technical and operational fields.

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