Journal articles: 'Interdiction networks'

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Gutin, Eli, Daniel Kuhn, and Wolfram Wiesemann. "Interdiction Games on Markovian PERT Networks." Management Science 61, no. 5 (2015): 999–1017. http://dx.doi.org/10.1287/mnsc.2014.1973.

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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 to exceed a specific threshold with the least resource consumption. In our model, threshold constraint is introduced as a trade-off between utility maximization and resource consumption, and stochastic cases with some known probability p of successful interdiction are considered. Existing algorithms do not perform well when dealing with threshold and stochastic constraints. To address the NP-hard problem, SSPIT-D, a decomposition approach based on Benders decomposition, was adopted. To optimize the master problem and subproblem iteration, an efficient dual subgraph interdiction algorithm SSPIT-S and a local research based better-response algorithm SSPIT-DL were designed, adding to the SSPIT-D. Numerical experiments on networks of different sizes and attributes were used to illustrate and validate the decomposition approach. The results showed that the dual subgraph and better-response procedure can significantly improve the efficiency and scalability of the decomposition algorithm. In addition, the improved enhancement algorithms are less sensitive and robust to parameters. Furthermore, the application in a real-world road network demonstrates the scalability of our decomposition approach.

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Held, Harald, and David L. Woodruff. "Heuristics for Multi-Stage Interdiction of Stochastic Networks." Journal of Heuristics 11, no. 5-6 (2005): 483–500. http://dx.doi.org/10.1007/s10732-005-3122-y.

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Yaghlane, Asma Ben, M. Naceur Azaiez, and Mehdi Mrad. "System survivability in the context of interdiction networks." Reliability Engineering & System Safety 185 (May 2019): 362–71. http://dx.doi.org/10.1016/j.ress.2019.01.005.

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Kosanoglu, Fuat, and Vicki M. Bier. "Target-oriented utility for interdiction of transportation networks." Reliability Engineering & System Safety 197 (May 2020): 106793. http://dx.doi.org/10.1016/j.ress.2020.106793.

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Ahumada-Paras, Mareldi, Kaarthik Sundar, Russell Bent, and Anatoly Zlotnik. "N-k interdiction modeling for natural gas networks." Electric Power Systems Research 190 (January 2021): 106725. http://dx.doi.org/10.1016/j.epsr.2020.106725.

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Jabarzare, Ziba, Hossein Zolfagharinia, and Mehdi Najafi. "Dynamic interdiction networks with applications in illicit supply chains." Omega 96 (October 2020): 102069. http://dx.doi.org/10.1016/j.omega.2019.05.005.

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Cuffe, Paul. "A Comparison of Malicious Interdiction Strategies Against Electrical Networks." IEEE Journal on Emerging and Selected Topics in Circuits and Systems 7, no. 2 (2017): 205–17. http://dx.doi.org/10.1109/jetcas.2017.2704879.

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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 case of bilevel optimization, for computational strain design and have developed a hybrid Benders algorithm (HBA) that deals with complicating binary variables in the model, thereby achieving high efficiency without numeric issues in search of best design strategies. More importantly, HBA can list solutions that meet users’ production requirements during the search, making it possible to obtain numerous design strategies at a small runtime overhead (typically ∼1 h, e.g. studied in this article). Availability and implementation Source code implemented in the MATALAB Cobratoolbox is freely available at https://github.com/chang88ye/NIHBA. Contact math4neu@gmail.com or natalio.krasnogor@ncl.ac.uk Supplementary information Supplementary data are available at Bioinformatics online.

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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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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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Bartolacci, Michael R., and Stanko Dimitrov. "Promoting Resiliency in Emergency Communication Networks." International Journal of Information Systems for Crisis Response and Management 9, no. 1 (2017): 1–10. http://dx.doi.org/10.4018/ijiscram.2017010101.

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Police, fire, and emergency personnel rely on wireless networks to serve the public. Whether it is during a natural disaster, or just an ordinary calendar day, wireless nodes of varying types form the infrastructure that local, regional, and even national scale agencies use to communicate while keeping the population served safe and secure. In this article, Michael R. Bartolacci and Stanko Dimitrov present a network interdiction modeling approach that can be utilized for analyzing vulnerabilities in public service wireless networks; subject to hacking, terrorism, or destruction from natural disasters. They develop a case study for wireless networks utilized by the sheriff's department of Miami-Dade County in Florida in the United States. Finally, the authors' modeling approach—given theoretical budgets for the “hardening” of wireless network nodes and for would-be destroyers of such nodes—highlights parts of the network where further investment may prevent damage and loss of capacity.

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Sundar, Kaarthik, Sidhant Misra, Russell Bent, and Feng Pan. "Credible Interdiction for Transmission Systems." IEEE Transactions on Control of Network Systems 8, no. 2 (2021): 738–48. http://dx.doi.org/10.1109/tcns.2021.3050128.

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Held, Harald, Raymond Hemmecke, and David L. Woodruff. "A decomposition algorithm applied to planning the interdiction of stochastic networks." Naval Research Logistics 52, no. 4 (2005): 321–28. http://dx.doi.org/10.1002/nav.20079.

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Shen, Siqian, J. Cole Smith, and Roshan Goli. "Exact interdiction models and algorithms for disconnecting networks via node deletions." Discrete Optimization 9, no. 3 (2012): 172–88. http://dx.doi.org/10.1016/j.disopt.2012.07.001.

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Goldberg, Noam. "Non-zero-sum nonlinear network path interdiction with an application to inspection in terror networks." Naval Research Logistics (NRL) 64, no. 2 (2017): 139–53. http://dx.doi.org/10.1002/nav.21738.

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Guvenc, Ismail, Farshad Koohifar, Simran Singh, Mihail L. Sichitiu, and David Matolak. "Detection, Tracking, and Interdiction for Amateur Drones." IEEE Communications Magazine 56, no. 4 (2018): 75–81. http://dx.doi.org/10.1109/mcom.2018.1700455.

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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 between an escaping adversary and a defender with multiple resources and real-time information available. Second, solving NEST is proven to be NP-hard. Third, after transforming the non-convex program of solving NEST to a linear program, we propose our incremental strategy generation algorithm, including: (i) novel pruning techniques in our best response oracle; and (ii) novel techniques for mapping strategies between subgames and adding multiple best response strategies at one iteration to solve extremely large problems. Finally, extensive experiments show the effectiveness of our approach, which scales up to realistic problem sizes with hundreds of nodes on networks including the real network of Manhattan.

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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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Lei, Ting L. "Evaluating the Vulnerability of Time-Sensitive Transportation Networks: A Hub Center Interdiction Problem." Sustainability 11, no. 17 (2019): 4614. http://dx.doi.org/10.3390/su11174614.

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Time-sensitive transportation systems have received increasing research attention recently. Examples of time-sensitive networks include those of perishable goods, high-value commodity, and express delivery. Much research has been devoted to optimally locating key facilities such as transportation hubs to minimize transit time. However, there is a lack of research attention to the reliability and vulnerability of time-sensitive transportation networks. Such issues cannot be ignored as facilities can be lost due to reasons such as extreme weather, equipment malfunction, and even intentional attacks. This paper proposes a hub interdiction center (HIC) model for evaluating the vulnerability of time-sensitive hub-and-spoke networks under disruptions. The model identifies the set of hub facilities whose loss will lead to the greatest increase in the worst-case transit time. From a planning perspective, such hubs are critical facilities that should be protected or enhanced by preventive measures. An efficient integer linear programming (ILP) formulation of the new model is developed. Computational experiments on a widely used US air passenger dataset show that losing a small number of hub facilities can double the maximum transit time.

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Lei, Ting L. "Identifying Critical Facilities in Hub-and-Spoke Networks: A Hub Interdiction Median Problem." Geographical Analysis 45, no. 2 (2013): 105–22. http://dx.doi.org/10.1111/gean.12006.

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Wu, Yipeng, Zhilong Chen, Huadong Gong, Qilin Feng, Yicun Chen, and Haizhou Tang. "Defender–attacker–operator: Tri-level game-theoretic interdiction analysis of urban water distribution networks." Reliability Engineering & System Safety 214 (October 2021): 107703. http://dx.doi.org/10.1016/j.ress.2021.107703.

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Swierczynski, Pawel, Marc Fyrbiak, Philipp Koppe, Amir Moradi, and Christof Paar. "Interdiction in practice—Hardware Trojan against a high-security USB flash drive." Journal of Cryptographic Engineering 7, no. 3 (2016): 199–211. http://dx.doi.org/10.1007/s13389-016-0132-7.

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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. An algorithm is proposed to solve the problem in strongly polynomial time. Computational experiments on two real-world datasets guarantee the efficiency and accuracy of the algorithm.

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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 communication cost, but the defender can strategically interdict some key edges on the physical layer given a certain budget of interdiction resources. This setting generalizes the classic single-layer shortest-path network interdiction problem, but brings in nonlinear objective functions, which are notoriously challenging to optimize. We reformulate the model and apply Benders decomposition process to solve this problem. A layer-mapping module is introduced to improve the decomposition algorithm and a random-search process is proposed to accelerate the convergence. Extensive numerical experiments demonstrate the computational efficiency of our methods.

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Gradoń, Kacper T., Janusz A. Hołyst, Wesley R. Moy, Julian Sienkiewicz, and Krzysztof Suchecki. "Countering misinformation: A multidisciplinary approach." Big Data & Society 8, no. 1 (2021): 205395172110138. http://dx.doi.org/10.1177/20539517211013848.

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The article explores the concept of infodemics during the COVID-19 pandemic, focusing on the propagation of false or inaccurate information proliferating worldwide throughout the SARS-CoV-2 health crisis. We provide an overview of disinformation, misinformation and malinformation and discuss the notion of “fake news”, and highlight the threats these phenomena bear for health policies and national and international security. We discuss the mis-/disinformation as a significant challenge to the public health, intelligence, and policymaking communities and highlight the necessity to design measures enabling the prevention, interdiction, and mitigation of such threats. We then present an overview of selected opportunities for applying technology to study and combat disinformation, outlining several approaches currently being used to understand, describe, and model the phenomena of misinformation and disinformation. We focus specifically on complex networks, machine learning, data- and text-mining methods in misinformation detection, sentiment analysis, and agent-based models of misinformation spreading and the detection of misinformation sources in the network. We conclude with the set of recommendations supporting the World Health Organization’s initiative on infodemiology. We support the implementation of integrated preventive procedures and internationalization of infodemic management. We also endorse the application of the cross-disciplinary methodology of Crime Science discipline, supplemented by Big Data analysis and related information technologies to prevent, disrupt, and detect mis- and disinformation efficiently.

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Liu, Chanjuan, Yuan Liu, Enqiang Zhu, Qiang Zhang, Xiaopeng Wei, and Bin Wang. "Cross-Inhibitor: a time-sensitive molecular circuit based on DNA strand displacement." Nucleic Acids Research 48, no. 19 (2020): 10691–701. http://dx.doi.org/10.1093/nar/gkaa835.

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Abstract Designing biochemical systems that can be effectively used in diverse fields, including diagnostics, molecular computing and nanomachines, has long been recognized as an important goal of molecular programming and DNA nanotechnology. A key issue in the development of such practical devices on the nanoscale lies in the development of biochemical components with information-processing capacity. In this article, we propose a molecular device that utilizes DNA strand displacement networks and allows interactive inhibition between two input signals; thus, it is termed a cross-inhibitor. More specifically, the device supplies each input signal with a processor such that the processing of one input signal will interdict the signal of the other. Biochemical experiments are conducted to analyze the interdiction performance with regard to effectiveness, stability and controllability. To illustrate its feasibility, a biochemical framework grounded in this mechanism is presented to determine the winner of a tic-tac-toe game. Our results highlight the potential for DNA strand displacement cascades to act as signal controllers and event triggers to endow molecular systems with the capability of controlling and detecting events and signals.

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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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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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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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Xiao, Kaiming, Cheng Zhu, Junjie Xie, Yun Zhou, Xianqiang Zhu, and Weiming Zhang. "Dynamic Defense against Stealth Malware Propagation in Cyber-Physical Systems: A Game-Theoretical Framework." Entropy 22, no. 8 (2020): 894. http://dx.doi.org/10.3390/e22080894.

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Stealth malware is a representative tool of advanced persistent threat (APT) attacks, which poses an increased threat to cyber-physical systems (CPS) today. Due to the use of stealthy and evasive techniques, stealth malwares usually render conventional heavy-weight countermeasures inapplicable. Light-weight countermeasures, on the other hand, can help retard the spread of stealth malwares, but the ensuing side effects might violate the primary safety requirement of CPS. Hence, defenders need to find a balance between the gain and loss of deploying light-weight countermeasures, which normally is a challenging task. To address this challenge, we model the persistent anti-malware process as a shortest-path tree interdiction (SPTI) Stackelberg game with both static version (SSPTI) and multi-stage dynamic version (DSPTI), and safety requirements of CPS are introduced as constraints in the defender’s decision model. The attacker aims to stealthily penetrate the CPS at the lowest cost (e.g., time, effort) by selecting optimal network links to spread, while the defender aims to retard the malware epidemic as much as possible. Both games are modeled as bi-level integer programs and proved to be NP-hard. We then develop a Benders decomposition algorithm to achieve the Stackelberg equilibrium of SSPTI, and design a Model Predictive Control strategy to solve DSPTI approximately by sequentially solving an 1+δ approximation of SSPTI. Extensive experiments have been conducted by comparing proposed algorithms and strategies with existing ones on both static and dynamic performance metrics. The evaluation results demonstrate the efficiency of proposed algorithms and strategies on both simulated and real-case-based CPS networks. Furthermore, the proposed dynamic defense framework shows its advantage of achieving a balance between fail-secure ability and fail-safe ability while retarding the stealth malware propagation in CPS.

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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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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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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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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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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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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 reducing the uncertainty, the actor wants to minimize the privacy information leakage by manipulating the asymmetric information and delay the goal recognition process. Then, the GRC under network interdiction is formulated as one static Stackelberg game, where the observer obtains asymmetric information about the actor’s intended goal and proactively interdicts the edges of the network with a bounded resource. The privacy leakage of the actor’s actions about the real goals is quantified by a min-entropy information metric and this privacy information metric is associated with the goal uncertainty. Next in importance, we define the privacy information metric based GRC under network interdiction (InfoGRC) and the information metric based GRC under threshold network interdiction (InfoGRCT). After dual reformulating, the InfoGRC and InfoGRCT as bi-level mixed-integer programming problems, one Benders decomposition-based approach is adopted to optimize the observer’s optimal interdiction resource allocation and the actor’s cost-optimal path-planning. Finally, some experimental evaluations are conducted to demonstrate the effectiveness of the InfoGRC and InfoGRCT models in the task of controlling the goal recognition process.

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Guerrero Castro, Javier. "Estudios de ignorancia, inteligencia y la guerra contra las drogas en Colombia/ Ignorance studies, intelligence and the war on drugs in Colombia." URVIO - Revista Latinoamericana de Estudios de Seguridad, no. 21 (November 13, 2017): 158. http://dx.doi.org/10.17141/urvio.21.2017.2943.

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Resumen Los enormes recursos invertidos en la inteligencia en la guerra contra las drogas han permitido a las agencias estatales acumular un importante conocimiento sobre el actuar de las redes internacionales del narcotráfico. La inteligencia juega un papel central en la planeación y conducción de operaciones de interdicción marítima. La utilización de la inteligencia en este tipo de operaciones tiene, sin embargo, limitaciones; y no menos importante, la interpretación y reinterpretación que se hace de los resultados. Propongo que los estudios de ignorancia pueden servir para explicar estos resultados paradójicos, así como el rol y los límites de la inteligencia en la guerra contra las drogas. En este artículo se reflexiona sobre las posibilidades de los estudios de ignorancia para entender las diferentes interpretaciones del uso de la inteligencia y los resultados de esta. El presente análisis se basa en una serie de entrevistas realizadas a miembros de la Armada en diversas partes del país.  Abstract The enormous resources invested in intelligence in the war on drugs have allowed state agencies to accumulate a valuable knowledge about the actions of international drug trafficking networks. Intelligence plays a central role in planning and conducting maritime interdiction operations. The use of intelligence in this type of operations, however, has limitations, not least the interpretation and reinterpretation of the results. I propose that studies of ignorance can serve to explain these paradoxical results, as well as the role and limits of intelligence in the war on drugs. In this article, we reflect on the possibilities of ignorance studies to understand the different interpretations of the use of intelligence and the results of this. The present analysis was based on a series of interviews with members of the Navy in various regions of the country.

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Muckensturm, Joshua R., and Dave C. Longhorn. "Assessing the vulnerability of military theater distribution routes." Journal of Defense Analytics and Logistics 3, no. 1 (2019): 60–82. http://dx.doi.org/10.1108/jdal-07-2018-0012.

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Purpose This paper introduces a new heuristic algorithm that aims to solve the military route vulnerability problem, which involves assessing the vulnerability of military cargo flowing over roads and railways subject to enemy interdiction. Design/methodology/approach Graph theory, a heuristic and a binary integer program are used in this paper. Findings This work allows transportation analysts at the United States Transportation Command to identify a relatively small number of roads or railways that, if interdicted by an enemy, could disrupt the flow of military cargo within any theater of operation. Research limitations/implications This research does not capture aspects of time, such as the reality that cargo requirements and enemy threats may fluctuate each day of the contingency. Practical implications This work provides military logistics planners and decision-makers with a vulnerability assessment of theater distribution routes, including insights into which specific roads and railways may require protection to ensure the successful delivery of cargo from ports of debarkation to final destinations. Originality/value This work merges network connectivity and flow characteristics with enemy threat assessments to identify militarily-useful roads and railways most vulnerable to enemy interdictions. A geographic combatant command recently used this specific research approach to support their request for rapid rail repair capability.

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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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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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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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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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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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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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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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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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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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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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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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Journal articles on the topic 'Interdiction networks'

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