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AbstractA common network security approach is to create a De‐Militarized Zone (DMZ) comprising two layers of network defense. The DMZ structure provides an extra layer of security between the sensitive information in a network (e.g., research and development files) and the component of the network that must interface with the general internet (e.g., the mail server). We consider a cyber‐attack on a DMZ network where both attacker and defender have limited resources and capabilities to attack and defend, respectively. We study two optimization problems and one game‐theoretic problem. Given that the attacker (defender) knows the potential capabilities of the defender (attacker) in the two layers, we obtain the optimal allocation of resources for the attacker (defender). The two‐optimization problems are not symmetrical. Absent any knowledge regarding the allocation of the adversary's resources, we solve a game‐theoretic problem and obtain some operational insights regarding the effect of combat (e.g., cyber) capabilities and their optimal allocation.

Firing multiple artillery rounds from the same location has two main benefits: a high rate of fire at the enemy and improved accuracy as the shooter's aim adjusts to previous rounds. However, firing repeatedly from the same location carries significant risk that the enemy will detect the artillery's location. Therefore, the shooter may periodically move locations to avoid counter‐battery fire. This maneuver is known as the shoot‐and‐scoot tactic. This article analyzes the shoot‐and‐scoot tactic for a time‐critical mission using Markov models. We compute optimal move policies and develop heuristics for more complex and realistic settings. Spending a reasonable amount of time firing multiple shots from the same location is often preferable to moving immediately after firing an initial salvo. Moving frequently reduces risk to the artillery, but also limits the artillery's ability to inflict damage on the enemy.

We construct a mathematical model of aerosol (i.e., droplet‐nuclei) transmission of influenza within a household containing one infected and embed it into an epidemic households model in which infecteds occasionally infect someone from another household; in a companion paper, we argue that the contribution from contact transmission is trivial for influenza and the contribution from droplet transmission is likely to be small. Our model predicts that the key infection control measure is the use of N95 respirators, and that the combination of respirators, humidifiers, and ventilation reduces the threshold parameter (which dictates whether or not an epidemic breaks out) by ≈20% if 70% of households comply, and by ≈40% if 70% of households and workplaces comply (≈28% reduction would have been required to control the 1918 pandemic). However, only ≈30% of the benefits in the household are achieved if these interventions are used only after the infected develops symptoms. It is also important for people to sleep in separate bedrooms throughout the pandemic, space permitting. Surgical masks with a device (e.g., nylon hosiery) to reduce face‐seal leakage are a reasonable alternative to N95 respirators if the latter are in short supply.

The article of record as published may be found at http://dx.doi.org/10.1080/1057610X.2010.484028 ; Motivated by the links between terror and crime and the difficulty in directly detecting terror activity, we formulate and solve a resource allocation problem on overlapping networks to determine if interdiction efforts may be able to take advantage of these connections. The government, knowing only the general structure and overlap of the networks, allocates its scarce resources to investigate each terror aand criminal network. There are two stages to the investigation: an initial investigation of all nodes (i.e., terrorists or criminals) and a secondary investigation of criminals identified during the initial investigation to determine if they are terrorists. Applying our model to data derived from a population of terrorists in the United States between 1971-2003 suggests that the government may be able to exploit the terror connections of crimes that are relatively uncommon, somewhat easy to detect, and are attractive to terrorists. ; Naval Postgraduate School, Monterey, CA; Stanford University, Stanford, CA

AbstractWe analyze an interdiction scenario where an interceptor attempts to catch an intruder as the intruder moves through the area of interest. A motivating example is the detection and interdiction of drug smuggling vessels in the Eastern Pacific and Caribbean. We study two models in this article. The first considers a nonstrategic target that moves through the area without taking evasive action to avoid the interdictor. We determine the optimal location the interceptor should position itself to best respond when a target arrives. The second model analyzes the strategic interaction between the interceptor and intruder using a Blotto approach. The intruder chooses a route to travel on and the interceptor chooses a route to patrol. We model the interaction as a two‐player game with a bilinear payoff function. We compute the optimal strategy for both players and examine several extensions. © 2017 Wiley Periodicals, Inc. Naval Research Logistics, 64: 29–40, 2017

The article of record as published may be found at https://doi.org/10.1002/nav.21730 ; We analyze an interdiction scenario where an interceptor attempts to catch an intruder as the intruder moves through the area of interest. A motivating example is the detection and interdiction of drug smuggling vessels in the Eastern Pacific and Caribbean. We study two models in this paper. The rst considers a non-strategic target that moves through the area without taking evasive action to avoid the interdictor. We determine the optimal location the interceptor should position itself to best respond when a target arrives. The second model analyzes the strategic interaction between the interceptor and intruder using a Blotto approach. The intruder chooses a route to travel on and the interceptor chooses a route to patrol. We model the inter- action as a two-player game with a bilinear payo function. We compute the optimal strategy for both players and examine several extensions.

Mathematical Social Sciences, V. 64, No. 3, pp 203-213. ; We formulate a rational choice model of popular behavior during an insurgency. An individual in the population either supports the insurgents or the government depending upon his attitude and the actions taken by each side. We focus on the effect of insurgency actions: benefits, impositions, and coercion. While benefits and impositions are applied uniformly throughout, the insurgents intend to only coerce those actively providing information to the government. However, due to the "fog of war", which may lead to limited situational awareness, the insurgents may mistakenly coerce their own supporters and potential drive them to aid the government. We examine how popular behavior varies under different situational awareness scenarios. When the insurgents have little situational awareness, they should take a few coercive actions. This implies that the government will be able to foster intelligence sources within the population. If the insurgents have perfect situational awareness, tipping points may occur that result in a significant reduction in active support for the government. In this case the government should take actions to decrease the coercing effectiveness of the insurgents and increase incentives to the population so they continue to provide information.

The article of record as published may be found at http://dx.doi.org/10.1080/01605682.2019.1570806 ; We study the problem of modelling the trajectory of a moving object of interest, or target, given limited locational and temporal information. Because of uncertainty in information, the location of the target can be represented using a spatial distribution, or heatmap. This paper proposes a comprehensive method for constructing and updating probability heatmaps for the location of a moving object based on uncertain information. This method uses Brownian bridges to model and construct temporal probability heatmaps of target movement, and employs a particle filter to update the heatmap as new intelligence arrives. This approach allows for more complexity than simple deterministic motion models, and is computationally easier to implement than detailed models for local target movement. ; Center for Multi-Intelligence Studies at the Naval Postgraduate School

Antisubmarine warfare (ASW) had been an important topic for military operations research (MOR) modelers and analysts during World War II and the Cold War. It became, however, somewhat out of vogue with the collapse of the Soviet Union and the subsequent reduction of the threat of submarine-related conflicts. In recent years, threats of such engagement have increased, in particular in the South China Sea. The re-emerging interest in this type of warfare, combined with new technologies and resulting tactics, pose a renewed challenge for MOR researchers. e study effective ways to operate a helicopter, equipped with dipping sonar (a dipper) in ASW missions. In particular, we examine the dipping pattern and frequency. A high rate of dipping is desirable as search effectiveness degrades in time as the search area expands. However, dipping too frequently results in overlap with previous dips, which may be wasteful. For a cookie-cutter sensor and a known constant submarine velocity, we prove that disjoint dips are optimal and generate the corresponding optimal dipping pattern. We analyze the effect of factors, such as helicopter speed, submarine speed, sensor detection radius, and travel time to the point of detection, on the optimal dipping pattern. We show that temporal parameters (submarine velocity and helicopter arrival time to the datum) are most critical. We also show that the no-overlap result is not always true; when the submarine's velocity is only known with probability, the optimal dipping frequency may include overlaps.

Inventory classification aims to ensure that business-driving inventory items are efficiently managed in spite of constrained resources. There are numerous single- and multiple-criteria approaches to it. Our objective is to improve resource allocation to focus on items that can lead to high equipment availability. This concern is typical of many service industries such as military logistics, airlines, amusement parks and public works. Our study tests several inventory prioritization techniques and finds that a modified multi-criterion weighted non-linear optimization (WNO) technique is a powerful approach for classifying inventory, outperforming traditional techniques of inventory prioritization such as ABC analysis in a variety of performance objectives.

The article of record as published may be found at http://dx.doi.org/10.12660/joscmv10n1p68-86 ; Inventory classification aims to ensure that business-driving inventory items are efficiently managed in spite of constrained resources. There are numerous single- and multiple-criteria approaches to it. Our objective is to improve resource allocation to focus on items that can lead to high equipment availability. This concern is typical of many service industries such as military logistics, airlines, amusement parks and public works. Our study tests several inventory prioritization techniques and finds that a modified multi-criterion weighted non-linear optimization (WNO) technique is a powerful approach for classifying inventory, outperforming traditional techniques of inventory prioritization such as ABC analysis in a variety of performance objectives.

European Journal of Operational Research, 215, pp 218-226 ; The article of record as published may be located at http://dx.doi.org/10.1016/j.ejor.2011.05.045 ; We present a sequential approach to detect static targets with imperfect sensors, which range from tower-mounted cameras to satellites. The scenario is operationally relevant to many military, homeland security, search and rescue, environmental engineering, counter-narcotics, and law enforcement applications. The idea is to stop the search as soon as there is enough probabilistic evidence about the targets' locations, given an operator-prescribed error tolerance, knowledge of the sensors' parameters, and a sequence of detection signals from the sensors. By stopping the search as soon as possible, we promote efficiency by freeing up sensors and operators to perform other tasks. The model we develop has the added benefits of decreasing operator workload and providing negative information as a search progresses.