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Journal articles on the topic 'Network interdiction'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 March 2023

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1

Mohammadi, Abumoslem, and Javad Tayyebi. "Maximum Capacity Path Interdiction Problem with Fixed Costs." Asia-Pacific Journal of Operational Research 36, no. 04 (2019): 1950018. http://dx.doi.org/10.1142/s0217595919500180.

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This paper addresses a network optimization interdiction problem, called the maximum capacity path interdiction problem. The problem is a hierarchical game containing two players: one evader and one interdictor. In a capacitated network, the evader wants to find a simple path from his current position to a target point with maximum capacity to send his forces along it while the interdictor decreases arc capacities under a budget constraint to interdict the advance of the evader’s forces as much as possible. This paper studies the case that each arc has a fixed cost for decreasing its capacity.

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2

Cormican, Kelly J., David P. Morton, and R. Kevin Wood. "Stochastic Network Interdiction." Operations Research 46, no. 2 (1998): 184–97. http://dx.doi.org/10.1287/opre.46.2.184.

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3

Wood, R. Kevin. "Deterministic network interdiction." Mathematical and Computer Modelling 17, no. 2 (1993): 1–18. http://dx.doi.org/10.1016/0895-7177(93)90236-r.

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4

Dahan, Mathieu, Saurabh Amin, and Patrick Jaillet. "Probability Distributions on Partially Ordered Sets and Network Interdiction Games." Mathematics of Operations Research 47, no. 1 (2022): 458–84. http://dx.doi.org/10.1287/moor.2021.1140.

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This article poses the following problem: Does there exist a probability distribution over subsets of a finite partially ordered set (poset), such that a set of constraints involving marginal probabilities of the poset’s elements and maximal chains is satisfied? We present a combinatorial algorithm to positively resolve this question. The algorithm can be implemented in polynomial time in the special case where maximal chain probabilities are affine functions of their elements. This existence problem is relevant for the equilibrium characterization of a generic strategic interdiction game on a

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5

Yan, Jingwen, Kaiming Xiao, Cheng Zhu, Jun Wu, Guoli Yang, and Weiming Zhang. "Bi-Layer Shortest-Path Network Interdiction Game for Internet of Things." Sensors 20, no. 20 (2020): 5943. http://dx.doi.org/10.3390/s20205943.

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Network security is a crucial challenge facing Internet-of-Things (IoT) systems worldwide, which leads to serious safety alarms and great economic loss. This paper studies the problem of malicious interdicting network exploitation of IoT systems that are modeled as a bi-layer logical–physical network. In this problem, a virtual attack takes place at the logical layer (the layer of Things), while the physical layer (the layer of Internet) provides concrete support for the attack. In the interdiction problem, the attacker attempts to access a target node on the logical layer with minimal communi

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6

Israeli, Eitan, and R. Kevin Wood. "Shortest-path network interdiction." Networks 40, no. 2 (2002): 97–111. http://dx.doi.org/10.1002/net.10039.

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7

Lunday, Brian J., and Hanif D. Sherali. "A Dynamic Network Interdiction Problem." Informatica 21, no. 4 (2010): 553–74. http://dx.doi.org/10.15388/informatica.2010.305.

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8

Hernandez Valencia, Juan Pablo, Jesus Maria Lopez-Lezama, and Bonie Johana Restrepo Cuestas. "Assessing the Vulnerability of Power Systems Using Multilevel Programming: A Literature Review." Revista Ingenierías Universidad de Medellín 20, no. 38 (2021): 99–117. http://dx.doi.org/10.22395/rium.v20n38a6.

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Vulnerability studies can identify critical elements in electric power systems in order to take protective measures against possible scenarios that may result in load shedding, which can be caused by natural events or deliberate attacks. This article is a literature review on the latter kind, i.e., the interdiction problem, which assumes there is a disruptive agent whose objective is to maximize the damage to the system, while the network operator acts as a defensive agent. The non-simultaneous interaction of these two agents creates a multilevel optimization problem, and the literature has re

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9

Wei, Xiangyu, Kai Xu, Peng Jiao, Quanjun Yin, and Yabing Zha. "A Decomposition Approach for Stochastic Shortest-Path Network Interdiction with Goal Threshold." Symmetry 11, no. 2 (2019): 237. http://dx.doi.org/10.3390/sym11020237.

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Shortest-path network interdiction, where a defender strategically allocates interdiction resource on the arcs or nodes in a network and an attacker traverses the capacitated network along a shortest s-t path from a source to a terminus, is an important research problem with potential real-world impact. In this paper, based on game-theoretic methodologies, we consider a novel stochastic extension of the shortest-path network interdiction problem with goal threshold, abbreviated as SSPIT. The attacker attempts to minimize the length of the shortest path, while the defender attempts to force it

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10

Soleimani-Alyar, Maryam, and Alireza Ghaffari-Hadigheh. "Dynamic Network Interdiction Problem with Uncertain Data." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 26, no. 02 (2018): 327–42. http://dx.doi.org/10.1142/s0218488518500174.

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This paper proposes an uncertain multi-period bi-level network interdiction problem with uncertain arc capacities. It is proved that there exists an equivalence relationship between uncertain multi-period network interdiction problem and the obtained deterministic correspondent. Application of the generalized Benders’ decomposition algorithm is considered as the solution approach to the resulting mixed-integer nonlinear programming problem. Finally, a numerical example is presented to illustrate the model and the algorithm.

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11

Zenklusen, R. "Network flow interdiction on planar graphs." Discrete Applied Mathematics 158, no. 13 (2010): 1441–55. http://dx.doi.org/10.1016/j.dam.2010.04.008.

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12

Pay, Babak Saleck, Jason R. W. Merrick, and Yongjia Song. "Stochastic network interdiction with incomplete preference." Networks 73, no. 1 (2018): 3–22. http://dx.doi.org/10.1002/net.21831.

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13

Chen, Yan, Cheng Guo, and Shenghan Yu. "Bi-objective optimization models for network interdiction." RAIRO - Operations Research 53, no. 2 (2019): 461–72. http://dx.doi.org/10.1051/ro/2017050.

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This paper designs models for the network interdiction problem. The interdiction problem under study has two contradicting goals: disrupting the network to minimize the profit of one set of agents, while as much as possible preserve the profit of another set of agents. Three bi-objective optimization methods are employed to form the optimal objectives. Also, we develop two formulations (MILP and multi-stage LP) used to deal with congestion cost which is a piecewise cost function. A numerical instance is also presented to better illustrate those models.

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14

Luo, Junren, Xiang Ji, Wei Gao, Wanpeng Zhang, and Shaofei Chen. "Goal Recognition Control under Network Interdiction Using a Privacy Information Metric." Symmetry 11, no. 8 (2019): 1059. http://dx.doi.org/10.3390/sym11081059.

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Goal recognition (GR) is a method of inferring the goals of other agents, which enables humans or AI agents to proactively make response plans. Goal recognition design (GRD) has been proposed to deliberately redesign the underlying environment to accelerate goal recognition. Along with the GR and GRD problems, in this paper, we start by introducing the goal recognition control (GRC) problem under network interdiction, which focuses on controlling the goal recognition process. When the observer attempts to facilitate the explainability of the actor’s behavior and accelerate goal recognition by

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15

Bud Whiteman, Philip S. "Improving Single Strike Effectiveness for Network Interdiction." Military Operations Research 4, no. 4 (1999): 15–30. http://dx.doi.org/10.5711/morj.4.4.15.

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16

Casas, Irene, Eric Delmelle, and Justin Yates. "Geographic characteristics of a network interdiction problem." GeoJournal 81, no. 1 (2014): 37–53. http://dx.doi.org/10.1007/s10708-014-9595-1.

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17

Wei, Xiangyu, Cheng Zhu, Kaiming Xiao, Quanjun Yin, and Yabing Zha. "Shortest Path Network Interdiction With Goal Threshold." IEEE Access 6 (2018): 29332–43. http://dx.doi.org/10.1109/access.2018.2838570.

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18

Amuru, SaiDhiraj, R. Michael Buehrer, and Mihaela van der Schaar. "Blind Network Interdiction Strategies—A Learning Approach." IEEE Transactions on Cognitive Communications and Networking 1, no. 4 (2015): 435–49. http://dx.doi.org/10.1109/tccn.2016.2542078.

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19

Bayrak, Halil, and Matthew D. Bailey. "Shortest path network interdiction with asymmetric information." Networks 52, no. 3 (2008): 133–40. http://dx.doi.org/10.1002/net.20236.

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20

Chestnut, Stephen R., and Rico Zenklusen. "Hardness and approximation for network flow interdiction." Networks 69, no. 4 (2017): 378–87. http://dx.doi.org/10.1002/net.21739.

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21

Bhandari, Phanindra Prasad, and Shree Ram Khadka. "Lexicographically Maximum Flows under an Arc Interdiction." Journal of Nepal Mathematical Society 4, no. 2 (2021): 8–14. http://dx.doi.org/10.3126/jnms.v4i2.41459.

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Network interdiction problem arises when an unwanted agent attacks the network system to deteriorate its transshipment efficiency. Literature is flourished with models and solution approaches for the problem. This paper considers a single commodity lexicographic maximum flow problem on a directed network with capacitated vertices to study two network flow problems under an arc interdiction. In the first, the objective is to find an arc on input network to be destroyed so that the residual lexicographically maximum flow is lexicographically minimum. The second problem aims to find a flow patter

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22

Zhao, Jia, and Gang Sun. "Reliable Network Interdiction Models with Multiple Unit Costs." Mathematical and Computational Applications 21, no. 4 (2016): 50. http://dx.doi.org/10.3390/mca21040050.

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23

Washburn, Alan, and Kevin Wood. "Two-Person Zero-Sum Games for Network Interdiction." Operations Research 43, no. 2 (1995): 243–51. http://dx.doi.org/10.1287/opre.43.2.243.

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24

Rocco S, Claudio M., and José Emmanuel Ramirez-Marquez. "Deterministic network interdiction optimization via an evolutionary approach." Reliability Engineering & System Safety 94, no. 2 (2009): 568–76. http://dx.doi.org/10.1016/j.ress.2008.06.008.

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25

Smith, J. Cole, and Yongjia Song. "A survey of network interdiction models and algorithms." European Journal of Operational Research 283, no. 3 (2020): 797–811. http://dx.doi.org/10.1016/j.ejor.2019.06.024.

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26

Murray, Alan T., Timothy C. Matisziw, and Tony H. Grubesic. "Critical network infrastructure analysis: interdiction and system flow." Journal of Geographical Systems 9, no. 2 (2007): 103–17. http://dx.doi.org/10.1007/s10109-006-0039-4.

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27

Sadeghi, Somayeh, Abbas Seifi, and Elnaz Azizi. "Trilevel shortest path network interdiction with partial fortification." Computers & Industrial Engineering 106 (April 2017): 400–411. http://dx.doi.org/10.1016/j.cie.2017.02.006.

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28

Bertsimas, Dimitris, Ebrahim Nasrabadi, and James B. Orlin. "On the power of randomization in network interdiction." Operations Research Letters 44, no. 1 (2016): 114–20. http://dx.doi.org/10.1016/j.orl.2015.11.005.

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29

Akgün, İbrahim, Barbaros Ç. Tansel, and R. Kevin Wood. "The multi-terminal maximum-flow network-interdiction problem." European Journal of Operational Research 211, no. 2 (2011): 241–51. http://dx.doi.org/10.1016/j.ejor.2010.12.011.

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30

Assimakopoulos, Nikitas. "A network interdiction model for hospital infection control." Computers in Biology and Medicine 17, no. 6 (1987): 413–22. http://dx.doi.org/10.1016/0010-4825(87)90060-6.

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31

Pavlikov, Konstantin. "Improved formulations for minimum connectivity network interdiction problems." Computers & Operations Research 97 (September 2018): 48–57. http://dx.doi.org/10.1016/j.cor.2018.04.012.

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32

Salmerón, Javier. "Deception tactics for network interdiction: A multiobjective approach." Networks 60, no. 1 (2011): 45–58. http://dx.doi.org/10.1002/net.20458.

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33

Jiang, Shouyong, Yong Wang, Marcus Kaiser, and Natalio Krasnogor. "NIHBA: a network interdiction approach for metabolic engineering design." Bioinformatics 36, no. 11 (2020): 3482–92. http://dx.doi.org/10.1093/bioinformatics/btaa163.

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Abstract Motivation Flux balance analysis (FBA) based bilevel optimization has been a great success in redesigning metabolic networks for biochemical overproduction. To date, many computational approaches have been developed to solve the resulting bilevel optimization problems. However, most of them are of limited use due to biased optimality principle, poor scalability with the size of metabolic networks, potential numeric issues or low quantity of design solutions in a single run. Results Here, we have employed a network interdiction model free of growth optimality assumptions, a special cas

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34

Claudio M., Rocco, Barker Kash, and Ramirez-Marquez Jose E. "Multi-Objective Power Grid Interdiction Model Considering Network Synchronizability." International Journal of Performability Engineering 17, no. 7 (2021): 609. http://dx.doi.org/10.23940/ijpe.21.07.p5.609618.

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35

Rocco S., Claudio M., and José Emmanuel Ramirez-Marquez. "A bi-objective approach for shortest-path network interdiction." Computers & Industrial Engineering 59, no. 2 (2010): 232–40. http://dx.doi.org/10.1016/j.cie.2010.04.004.

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36

Royset, Johannes O., and R. Kevin Wood. "Solving the Bi-Objective Maximum-Flow Network-Interdiction Problem." INFORMS Journal on Computing 19, no. 2 (2007): 175–84. http://dx.doi.org/10.1287/ijoc.1060.0191.

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37

Borndörfer, Ralf, Guillaume Sagnol, and Stephan Schwartz. "An Extended Network Interdiction Problem for Optimal Toll Control." Electronic Notes in Discrete Mathematics 52 (June 2016): 301–8. http://dx.doi.org/10.1016/j.endm.2016.03.040.

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38

Smith, J. Cole, Churlzu Lim, and Fransisca Sudargho. "Survivable network design under optimal and heuristic interdiction scenarios." Journal of Global Optimization 38, no. 2 (2006): 181–99. http://dx.doi.org/10.1007/s10898-006-9067-3.

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39

Zhang, Pengfei, and Neng Fan. "Analysis of budget for interdiction on multicommodity network flows." Journal of Global Optimization 67, no. 3 (2016): 495–525. http://dx.doi.org/10.1007/s10898-016-0422-8.

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40

Ramirez-Marquez, José Emmanuel, and Claudio M. Rocco S. "Stochastic network interdiction optimization via capacitated network reliability modeling and probabilistic solution discovery." Reliability Engineering & System Safety 94, no. 5 (2009): 913–21. http://dx.doi.org/10.1016/j.ress.2008.10.006.

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41

Zhang, Kaike, Xueping Li, and Mingzhou Jin. "Efficient Solution Methods for a General r-Interdiction Median Problem with Fortification." INFORMS Journal on Computing 34, no. 2 (2022): 1272–90. http://dx.doi.org/10.1287/ijoc.2021.1111.

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This study generalizes the r-interdiction median (RIM) problem with fortification to simultaneously consider two types of risks: probabilistic exogenous disruptions and endogenous disruptions caused by intentional attacks. We develop a bilevel programming model that includes a lower-level interdiction problem and a higher-level fortification problem to hedge against such risks. We then prove that the interdiction problem is supermodular and subsequently adopt the cuts associated with supermodularity to develop an efficient cutting-plane algorithm to achieve exact solutions. For the fortificati

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42

He, Min, Guang-Xun Du, Xiaoyi Zhang, and Zheng Zheng. "A Cooperative Network Interdiction Model and Its Optimization Solution Algorithm." International Journal of Computational Intelligence Systems 11, no. 1 (2018): 560. http://dx.doi.org/10.2991/ijcis.11.1.41.

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43

Carrigy, A., J. E. Ramirez-Marquez, and C. M. Rocco. "Multistate stochastic network interdiction via reliability modelling and evolutionary optimization." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 224, no. 1 (2009): 27–42. http://dx.doi.org/10.1243/1748006xjrr254.

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44

Lim, Churlzu, and J. Cole Smith. "Algorithms for discrete and continuous multicommodity flow network interdiction problems." IIE Transactions 39, no. 1 (2007): 15–26. http://dx.doi.org/10.1080/07408170600729192.

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45

Yates, Justin, and Kavitha Lakshmanan. "A constrained binary knapsack approximation for shortest path network interdiction." Computers & Industrial Engineering 61, no. 4 (2011): 981–92. http://dx.doi.org/10.1016/j.cie.2011.06.011.

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46

Sullivan, Kelly M., J. Cole Smith, and David P. Morton. "Convex hull representation of the deterministic bipartite network interdiction problem." Mathematical Programming 145, no. 1-2 (2013): 349–76. http://dx.doi.org/10.1007/s10107-013-0650-3.

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47

Laan, Corine M., Tom van der Mijden, Ana Isabel Barros, Richard J. Boucherie, and Herman Monsuur. "An interdiction game on a queueing network with multiple intruders." European Journal of Operational Research 260, no. 3 (2017): 1069–80. http://dx.doi.org/10.1016/j.ejor.2017.02.035.

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48

Janjarassuk, Udom, and Jeff Linderoth. "Reformulation and sampling to solve a stochastic network interdiction problem." Networks 52, no. 3 (2008): 120–32. http://dx.doi.org/10.1002/net.20237.

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49

Afshari Rad, Maria, and Hossein Taghizadeh Kakhki. "Two extended formulations for cardinality maximum flow network interdiction problem." Networks 69, no. 4 (2017): 367–77. http://dx.doi.org/10.1002/net.21732.

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50

Zhang, Youzhi, Qingyu Guo, Bo An, Long Tran-Thanh, and Nicholas R. Jennings. "Optimal Interdiction of Urban Criminals with the Aid of Real-Time Information." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1262–69. http://dx.doi.org/10.1609/aaai.v33i01.33011262.

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Most violent crimes happen in urban and suburban cities. With emerging tracking techniques, law enforcement officers can have real-time location information of the escaping criminals and dynamically adjust the security resource allocation to interdict them. Unfortunately, existing work on urban network security games largely ignores such information. This paper addresses this omission. First, we show that ignoring the real-time information can cause an arbitrarily large loss of efficiency. To mitigate this loss, we propose a novel NEtwork purSuiT game (NEST) model that captures the interaction

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