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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Optimization Models and Algorithms for Vulnerability Analysis and Mitigation Planning of Pyro-Terrorism

Rashidi, Eghbal 12 August 2016 (has links)
In this dissertation, an important homeland security problem is studied. With the focus on wildfire and pyro-terrorism management. We begin the dissertation by studying the vulnerability of landscapes to pyro-terrorism. We develop a maximal covering based optimization model to investigate the impact of a pyro-terror attack on landscapes based on the ignition locations of fires. We use three test case landscapes for experimentation. We compare the impact of a pyro-terror wildfire with the impacts of naturally-caused wildfires with randomly located ignition points. Our results indicate that a pyro-terror attack, on average, has more than twice the impact on landscapes than wildfires with randomly located ignition points. In the next chapter, we develop a Stackelberg game model, a min-max network interdiction framework that identifies a fuel management schedule that, with limited budget, maximally mitigates the impact of a pyro-terror attack. We develop a decomposition algorithm called MinMaxDA to solve the model for three test case landscapes, located in Western U.S. Our results indicate that fuel management, even when conducted on a small scale (when 2% of a landscape is treated), can mitigate a pyro-terror attack by 14%, on average, comparing to doing nothing. For a fuel management plan with 5%, and 10% budget, it can reduce the damage by 27% and 43% on average. Finally, we extend our study to the problem of suppression response after a pyro-terror attack. We develop a max-min model to identify the vulnerability of initial attack resources when used to fight a pyro-terror attack. We use a test case landscape for experimentation and develop a decomposition algorithm called Bounded Decomposition Algorithm (BDA) to solve the problem since the model has bilevel max-min structure with binary variables in the lower level and therefore not solvable by conventional methods. Our results indicate that although pyro-terror attacks with one ignition point can be controlled with an initial attack, pyro-terror attacks with two and more ignition points may not be controlled by initial attack. Also, a faster response is more promising in controlling pyro-terror fires.
2

Pricing in a Multiple ISP Environment with Delay Bounds and Varying Traffic Loads

Gabrail, Sameh January 2008 (has links)
In this thesis, we study different Internet pricing schemes and how they can be applied to a multiple ISP environment. We first take a look at the current Internet architecture. Then the different classes that make up the Internet hierarchy are discussed. We also take a look at peering among Internet Service Providers (ISPs) and when it is a good idea for an ISP to consider peering. Moreover, advantages and disadvantages of peering are discussed along with speculations of the evolution of the Internet peering ecosystem. We then consider different pricing schemes that have been proposed and study the factors that make up a good pricing plan. Finally, we apply some game theoretical concepts to discuss how different ISPs could interact together. We choose a pricing model based on a Stackelberg game that takes into consideration the effect of the traffic variation among different customers in a multiple ISP environment. It allows customers to specify their desired QoS in terms of maximum allowable end-to-end delay. Customers only pay for the portion of traffic that meet this delay bound. Moreover, we show the effectiveness of adopting this model through a comparison with a model that does not take traffic variation into account. We also develop a naïve case and compare it to our more sophisticated approach.
3

Pricing in a Multiple ISP Environment with Delay Bounds and Varying Traffic Loads

Gabrail, Sameh January 2008 (has links)
In this thesis, we study different Internet pricing schemes and how they can be applied to a multiple ISP environment. We first take a look at the current Internet architecture. Then the different classes that make up the Internet hierarchy are discussed. We also take a look at peering among Internet Service Providers (ISPs) and when it is a good idea for an ISP to consider peering. Moreover, advantages and disadvantages of peering are discussed along with speculations of the evolution of the Internet peering ecosystem. We then consider different pricing schemes that have been proposed and study the factors that make up a good pricing plan. Finally, we apply some game theoretical concepts to discuss how different ISPs could interact together. We choose a pricing model based on a Stackelberg game that takes into consideration the effect of the traffic variation among different customers in a multiple ISP environment. It allows customers to specify their desired QoS in terms of maximum allowable end-to-end delay. Customers only pay for the portion of traffic that meet this delay bound. Moreover, we show the effectiveness of adopting this model through a comparison with a model that does not take traffic variation into account. We also develop a naïve case and compare it to our more sophisticated approach.
4

A game-theoretic and machine-learning approach to demand response management for the smart grid

Meng, Fanlin January 2015 (has links)
Demand Response (DR) was proposed more than a decade ago to incentivise customers to shift their electricity usage from peak demand periods to off-peak demand periods and to curtail their electricity usage during peak demand periods. However, the lack of two-way communication infrastructure weakens the influence of DR and limits its applications. With the development of smart grid facilities (e.g. smart meters and the two-way communication infrastructure) that enable the interactions between the energy retailer and its customers, demand response shows great potential to reduce customers' bills, increase the retailer's profit and further stabilize the power systems. Given such a context, in this thesis we propose smart pricing based demand response programs to study the interactions between the energy retailer and its customers based on game-theory and machine learning techniques. We conduct the research in two different application scenarios: 1) For customers with home energy management system (HEMS) installed in their smart meters, the retailer will know the customers' energy consumption patterns by interacting with the HEMS. As a result, the smart pricing based demand response problem can be modelled as a Stackelberg game or bilevel optimization problem. Further, efficient solutions are proposed for the demand response problems and the existence of optimal solution to the Stackelberg game and the bilevel model is proved; 2) For customers without HEMS installed in their smart meters, the retailer will not know the energy consumption patterns of these customers and must learn customers' behaviour patterns via historical energy usage data. To realize this, two appliance-level machine learning algorithms are proposed to learn customers' consumption patterns. Further, distributed pricing algorithms are proposed for the retailer to solve the demand response problem effectively. Simulation results indicate the effectiveness of the proposed demand response models in both application scenarios.
5

Interchange fee rate, merchant discount rate, and retail prices in a credit card network : a game-theoretic analysis

GUO, Hangfei 01 January 2011 (has links)
We consider two game-theoretic settings to determine the optimal values of an issuer's interchange fee rate, an acquirer's merchant discount rate, and a merchant's retail prices for multiple products in a credit card network. In the first setting, we investigate a two-stage game problem in which the issuer and the acquirer first negotiate the interchange fee rate, and the acquirer and the retailer then determine their merchant discount rate and retail prices, respectively. In the second setting, motivated by the recent U.S. bill "H.R. 2695," we develop a three-player cooperative game in which the issuer, the acquirer, and the merchant form a grand coalition and bargain over the interchange fee rate and the merchant discount rate. Following the cooperative game, the retailer makes its retail pricing decisions. We derive both the Shapley value- and the nucleolus-characterized unique rates for the grand coalition. Comparing the two game settings, we show that the participation of the merchant in the negotiation process can result in the reduction of both rates. Moreover, the stability of the grand coalition in the cooperative game setting may require that the merchant should delegate the credit card business only to the issuer and the acquirer with sufficiently low operation costs. We also find that the large, highly-specialized merchants and banks are more likely to join the cooperative negotiation whereas the small firms may prefer the two-stage game setting. Our numerical experiments demonstrate that the acquirer's and the issuer's unit operation costs more significantly impact both rates in the cooperative game setting than in the two-stage game setting.
6

Two-person games for stochastic network interdiction : models, methods, and complexities

Nehme, Michael Victor 27 May 2010 (has links)
We describe a stochastic network interdiction problem in which an interdictor, subject to limited resources, installs radiation detectors at border checkpoints in a transportation network in order to minimize the probability that a smuggler of nuclear material can traverse the residual network undetected. The problems are stochastic because the smuggler's origin-destination pair, the mass and type of material being smuggled, and the level of shielding are known only through a probability distribution when the detectors are installed. We consider three variants of the problem. The first is a Stackelberg game which assumes that the smuggler chooses a maximum-reliability path through the network with full knowledge of detector locations. The second is a Cournot game in which the interdictor and the smuggler act simultaneously. The third is a "hybrid" game in which only a subset of detector locations is revealed to the smuggler. In the Stackelberg setting, the problem is NP-complete even if the interdictor can only install detectors at border checkpoints of a single country. However, we can compute wait-and-see bounds in polynomial time if the interdictor can only install detectors at border checkpoints of the origin and destination countries. We describe mixed-integer programming formulations and customized branch-and-bound algorithms which exploit this fact, and provide computational results which show that these specialized approaches are substantially faster than more straightforward integer-programming implementations. We also present some special properties of the single-country case and a complexity landscape for this family of problems. The Cournot variant of the problem is potentially challenging as the interdictor must place a probability distribution over an exponentially-sized set of feasible detector deployments. We use the equivalence of optimization and separation to show that the problem is polynomially solvable in the single-country case if the detectors have unit installation costs. We present a row-generation algorithm and a version of the weighted majority algorithm to solve such instances. We use an exact-penalty result to formulate a model in which some detectors are visible to the smuggler and others are not. This may be appropriate to model "decoy" detectors and detector upgrades. / text
7

Mathematical programming analyses of an established timberlands supply chain with interests in biofuel investments

Yeh, Kevin 12 January 2015 (has links)
In the push for clean and renewable fuels, timber derived biomass is a promising frontier for biofuel production in the United States. This thesis approaches the established timberlands biofuel implementation problem with three different mathematical programming studies, each testing feasibility and sustainability in different economic and supply related situations. In the first study, a competitive game theory approach was utilized to provide new insights into the behavior within a timberlands supply chain. We utilized Stackelberg game theory modeled with bilevel programming to represent the competing harvesting and manufacturing sectors. In the second study, the initial bilevel model was utilized in a larger two stage multiperiod model with parameter uncertainty. In this more realistic model, the first stage contained logistical decisions around biorefinery investments, such as location and capacity, while the second stage was composed of multiple discrete bilevel scenarios representing potential situations in the timberlands system. The final study focused on long term land management strategies for the timberlands supply chain. Introduction of a new biorefinery investment meant that management strategies must be altered to ensure consistent material flows to manufacturers as well as sustain the new production facility. A modified cyclic scheduling formulation was used to model a timberlands system and its planting and harvesting schedule to accommodate a new biorefinery. This cyclic model added an initial startup period to initiate biofuel production and provide time to adapt land management. The overall contribution of these studies was to analyze a biorefinery's impact on the established behavior in a timberlands supply chain. In particular, the goals of these models were to develop introductory decision making tools for timberlands supply chain managers.
8

Resource Allocation in Femtocells via Game Theory

Sankar, V Udaya January 2015 (has links) (PDF)
Most of the cellular tra c (voice and data) is generated indoors. Due to attenuation from walls, quality of service (QoS) of di erent applications degrades for indoor tra c. Thus in order to provide QoS for such users the Macro base station (MBS) has to transmit at high power. This increases recurring costs to the service provider and contributes to green house emissions. Hence, Femtocells (FC) are considered as an option. Femto Access Points (FAP) are low cost, low powered, small base stations deployed indoors by customers. A substantial part of indoor tra c is diverted from the Macrocell (MC) through the FAP. Since the FCs also use the same channels as the MC, deployment of FCs causes interference to not only its neighbouring FCs but also to the users in the MC. Thus, we need better interference management techniques for this system. In this thesis, we consider a system with multiple Femtocells operating in a Macrocell. FCs and MC use same set of multiple channels and support multiple users. Each user may have a minimum rate requirement. To limit interference to the MC, there is a peak power constraint on each channel. In the rst part of the thesis, we consider sparsely deployed FCs where the interference between the FCs is negligible. For this we formulate the problem of channel allocation and power control in each FC. We develop computationally e cient, suboptimal algorithms to satisfy QoS of each user in the FC. If QoS of each user is not satis ed, we provide solutions which are fair to all the users. In the second part of the thesis, we consider the case of densely deployed FCs where we formulate the problem of channel allocation and power control in each Femtocell as a noncooperative Game. We develop e cient decentralized algorithms to obtain a Nash equilibrium (NE) at which QoS of each user is satis ed. We also obtain e cient decentralized algorithms to obtain fair NE when it may not be feasible to satisfy the QoS of all the users in the FC. Finally, we extend our algorithms to the case where there may be voice and data users in the system. In the third part of the thesis, we continue to study the problem setup in the second part, where we develop algorithms which can simultaneously consider the cases where QoS of users can be satis ed or not. We provide algorithms to compute Coarse Correlated Equilibrium (CCE), Pareto optimal points and Nash bargaining solutions. In the nal part of the thesis, we consider interference limit at the MBS and model FCs as sel sh nodes. The MBS protects itself via pricing subchannels per usage. We obtain a Stackelberg equilibrium (SE) by considering MBS as a leader and FCs as followers.
9

Mechanism Design in Defense against Offline Password Attacks

Wenjie Bai (16051163) 15 June 2023 (has links)
<p>The prevalence of offline password attacks, resulting from attackers breaching authentication servers and stealing cryptographic password hashes, poses a significant threat. Users' tendency to select weak passwords and reuse passwords across multiple accounts, coupled with computation advancement,  further exacerbate the danger.</p> <p><br></p> <p>This dissertation addresses this issue by proposing password authentication mechanisms that aim to minimize the number of compromised passwords in the event of offline attacks, while ensuring that the server's workload remains manageable. Specifically, we present three mechanisms: (1) DAHash: This mechanism adjusts password hashing costs based on the strength of the underlying password. Through appropriate tuning of hashing cost parameters, the DAHash mechanism effectively reduces the fraction of passwords that can be cracked by an offline password cracker. (2) Password Strength Signaling: We explore the application of Bayesian Persuasion to password authentication. The key idea is to have the authentication server store a noisy signal about the strength of each user password for an offline attacker to find. We demonstrate that by appropriately tuning the noise distribution for the signal, a rational attacker will crack fewer passwords. (3) Cost-Asymmetric Memory Hard Password Hashing: We extend the concept of password peppering to modern Memory Hard password hashing algorithms. We identify limitations in naive extensions and introduce the concept of cost-even breakpoints as a solution. This approach allows us to overcome these limitations and achieve cost-asymmetry, wherein the expected cost of validating a correct password is significantly smaller than the cost of rejecting an incorrect password.</p> <p><br></p> <p>When analyzing the behavior of a rational attacker it is important to understand the attacker’s guessing curve i.e., the percentage of passwords that the attacker could crack within a guessing budget B. Dell’Amico and Filippone introduced a Monte Carlo algorithm to estimate the guessing number of a password as well as an estimate for the guessing curve. While the estimated guessing number is accurate in expectation the variance can be large and the method does not guarantee that the estimates are accurate with high probability. Thus, we introduce Confident Monte Carlo as a tool to provide confidence intervals for guessing number estimates and upper/lower bound the attacker’s guessing curves.</p> <p><br></p> <p>Moreover, we extend our focus beyond classical attackers to include quantum attackers. We present a decision-theoretic framework that models the rational behavior of attackers equipped with quantum computers. The objective is to quantify the capabilities of a rational quantum attacker and the potential damage they could inflict, assuming optimal decision-making. Our framework can potentially contribute to the development of effective countermeasures against a wide range of quantum pre-image attacks in the future.</p>
10

Essays on agricultural and environmental policy

Jonsson, Thomas January 2007 (has links)
<p>This thesis consists of a summary and four papers. The first two papers address political economy and indus-trial organization aspects of agricultural policy, and the last two international aspects of environmental policy.</p><p>Paper [I] explains Common Agricultural Policy (CAP) subsidies to farmers by the influence of farmer interest-groups with an EU-wide membership. The analysis is based on panel-data for fifteen commodities over the period 1986-2003. Because the CAP is set as an overall EU policy, effective lobbying presents a collective ac-tion problem to the farmers in the EU as a whole. Indicators of lobbying, which are based on this perception, are found to explain part of the variation in agricultural support.</p><p>In Paper [II], the Bresnahan-Lau framework is used to analyze whether policy reforms, i.e. the two-price sys-tem (an input quota, 1986-1991) and a general deregulation of dairy policy (1991-1994) had any market power effects on the Swedish butter market. The results show that the null hypothesis of no market power cannot be rejected, for any of the specific policy reforms, at any reasonable significance level.</p><p>Paper [III] concerns the welfare consequences of environmental policy cooperation. It is assumed that coun-tries finance their public expenditures by using distortionary taxes, and that they differ with respect to compe-tition in the labor market. It is shown how the welfare effect of an increase in the expenditures on abatement depends on changes in the environmental damage, employment and work hours. The welfare effect is also related to the strategic interaction among the countries in the prereform equilibrium.</p><p>In Paper [IV] environmental policy in an economic federation, where each national government faces a mixed tax problem, is addressed. It is assumed that the federal government sets emission targets, which are imple-mented at the national level. It is also assumed that the economic federation is decentralized. The results high-light a strategic role of income and commodity taxation, i.e. each country uses its policy instruments, at least in part, to influence the emission target.</p>

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