The focus of this thesis is on cooperative spectrum sensing and related security issues in multi-channel cognitive radio networks (MCCRNs). We first study the channel assignment for cooperative spectrum sensing in MCCRNs to maximize the number of available channels. In centralized implementation, a heuristic scheme is proposed along with a greedy scheme to reduce the reported information from the cognitive radios (CRs). In distributed scenario, a novel scheme with multi-round operation is designed following the coalitional game theory. Next, we focus on the physical layer security issues for cooperative spectrum sensing in MCCRNs, caused by Byzantine attacks. New counterattacks are proposed to combat attacks comprising coalition head and CRs as Byzantine attackers, which target to reduce the number of available channels for sensing in distributed MCCRNs. First, a new secure coalition head selection is proposed, by using statistical properties of the exchanged SNRs in the coalitions. Then, an iterative algorithm is proposed to block out attackers, if they continue attacking the system. The important problem of key management is considered next, and an energy-efficient identity-based and a certificate-based distributed key management schemes are proposed. First, a new elliptic curve cryptography (ECC)-based distributed private key generation scheme is proposed to combat the single point of failure problem along with novel distributed private key generator (DPKG) selection schemes to preserve security and energy-efficiency. Because of its importance in the proposed identity-based key management scheme, we further propose a low-complexity DPKG assignment, based on multi-objective programming, which can capture DPKG fairness in addition to energy-efficiency. Finally, a more powerful and intelligent distributed cooperative Byzantine attack on the proposed multi-channel cooperative spectrum sensing is proposed, where attackers collude by applying coalitional game theory to maximize the number of invaded channels in a distributed manner. As a remedy, a hierarchical identity-based key management scheme is proposed, in which CRs can only play on a certain number of requested channels and channel access for sensing is limited to the honest CRs selected in the coalitional game. Simulation results show that the proposed schemes can significantly improve cooperative spectrum sensing and secure the system against Byzantine attacks.

An assessment of the costs and benefits of antiterrorism policies has a number of challenges. Canada’s situation is particularly complex because of its asymmetric and integrative economic and geographic relationship with the United States. Few theoretical models of international antiterrorism policy exist and none fit well this asymmetric relationship. This thesis attempts to fill this void by understanding the motives behind Canadian antiterrorism policies and the related spending, and how these are affected by the relationship with the United States. We explore a two country theoretical model with strategic interactions, focusing on relative spending. What is found is that the economics and geography of the two countries play a large role in motivating spending and can drastically alter antiterrorism spending beyond that needed solely for public safety in Canada. / Graduate / 0511 / 0501 / 0616 / kyleburak@gmail.com

We first present a non-cooperative auction game to solve the bandwidth allocation problem for non-cooperative channel access in a wireless network. The Nash equilibrium is obtained as a solution of the game. To address this problem of bandwidth sharing under unknown information, we further develop a Bayesian auction game model and then Bayesian Nash equilibrium is then obtained. Next, we present a framework based on coalitional game for cooperative channel access for carry-and-forward-based data delivery. Each mobile node helps others to carry and then forward their data. A coalitional game is proposed to find a stable coalition structure for this cooperative data delivery. We next present static and dynamic coalitional games for carry-and-forward-based data delivery when the behavior of each mobile node is unknown by others. In the dynamic game, each mobile node can update its beliefs about other mobile nodes’ types when the static coalitional game is played repeatedly.

State and local governments forfeit over $80 billion in tax revenue each year in order to incentivize businesses to expand operations and create jobs in, relocate to, or refrain from leaving their states. The use of tax incentives has expanded massively during recent decades to include all states and a range of industries. Targeted tax incentives are proven to be an inefficient method of promoting economic growth and job creation, because of the negative impact of public spending cuts that offset the decline in revenue. There is a large disparity between states that do offer large amounts of incentives and those that do not that remains largely unexplained in the literature. Using cross-sectional data from the New York Times, I examine whether this disparity is associated with the political economy or geography of the states, or if it is largely random. I find little support for the political economy and geography hypotheses. A lack of support for the first two hypotheses suggests that the use of business tax incentives is largely random at the state level. I conclude by examining the viability of several proposals for limiting the use of business incentives and suggest more data collection and further research into potential solutions.

The thesis investigates a number of auction formats both theoretically and empirically. The effect of different auction rules on the final price and on bidder valuations is analyzed. Results from an experimental sale of real goods, testing revenue equivalence of the open and sealed-bid second-price auction do not conform to theoretical predictions: the open auction leading to significantly lower prices than the sealed-bid auction. It turns out that the open auction format allows bidders to satisfy a tendency to "stick together" with their valuations. The empirical results motivate a dynamic bidding model of interdependent valuations, bidders being uncertain about their valuations and learning from the exit-prices of their rivals. Furthermore, bidding behavior on the Internet is investigated in the hard close and the automatically extended auction. Late bidding is shown to be a rational strategy in the hard close auction, but not in the automatically extended auction. Theoretical results show that the expected final price and seller revenue is lower in the hard close auction than in the automatically extended auction, where prestige-concerns can lead to an explosive final price. Moreover, Yahoo auction data confirms the strong presence of late bidding in the hard-close auction and the seller's preference for the automatically extended auction. (author´s abstract)

An assessment of the costs and benefits of antiterrorism policies has a number of challenges. Canada’s situation is particularly complex because of its asymmetric and integrative economic and geographic relationship with the United States. Few theoretical models of international antiterrorism policy exist and none fit well this asymmetric relationship. This thesis attempts to fill this void by understanding the motives behind Canadian antiterrorism policies and the related spending, and how these are affected by the relationship with the United States. We explore a two country theoretical model with strategic interactions, focusing on relative spending. What is found is that the economics and geography of the two countries play a large role in motivating spending and can drastically alter antiterrorism spending beyond that needed solely for public safety in Canada. / Graduate / 0511 / 0501 / 0616 / kyleburak@gmail.com

An arms-race exists between malware authors and system defenders in which defenders develop new detection approaches only to have the malware authors develop new techniques to bypass them. This motivates the need for a formal framework to jointly reason about malware and its detection. This dissertation presents such a formal framework termed the extended Maurer model} (EMM) and then applies this framework to develop a game-theoretic model of the malware authors versus system defenders confrontation. To be inclusive of modern computers and networks, the EMM has been developed by extending to the existing Maurer computer model, a Turing-reducible model of computer operations. The basic components of the Maurer model have been extended to incorporate the necessary structures to enable the modeling of programs, concurrency, multiple processors, and networks. In particular, we show that the proposed EMM remains a Turing equivalent model which is able to model modern computers, computer networks, as well as complex programs such as modern virtual machines and web browsers. Through the proposed EMM, we provide formalizations for the violations of the standard security policies. Specifically, we provide the definitions of the violations of confidentiality policies, integrity policies, availability policies, and resource usage policies. Additionally, we also propose formal definitions of a number of common malware classes, including viruses, Trojan horses, spyware, bots, and computer worms. We also show that the proposed EMM is complete in terms of its ability to model all implementable that could exist malware within the context of a given defended environment. We then use the EMM to evaluate and analyze the resilience of a number of common malware detection approaches. We show that static anti-malware signature scanners can be easily evaded by obfuscation, which is consistent with the results of prior experimental work. Additionally, we also use the EMM to formally show that malware authors can avoid detection by dynamic system call sequence detection approaches, which also agrees with recent experimental work. A measure-theoretic model of the EMM is then developed by which the completeness of the EMM with respect to its ability to model all implementable malware detection approaches is shown. Finally, using the developed EMM, we provide a game-theoretic model of the confrontation of malware authors and system defenders. Using this game model, under game theory's strict dominance solution concept, we show that rational attackers are always required to develop malware that is able to evade the deployed malware detection solutions. Moreover, we show that the attacker and defender adaptations can be modeled as a sequence of iterative games. Hence, the question can be asked as to the conditions required if such a sequence (or arms-race) is to converge towards a defender advantageous end-game. It is shown via the EMM that, in the general context, this desired situation requires that the next attacker adaptation exists as, at least, a computationally hard problem. If this is not the case, then we show via the EMM's measure theory perspective, that the defender is left needing to track statistically non-stationary attack behaviors. Hence, by standard information theory constructs, past attack histories can be shown to be uninformative with respect to the development of the next to be required adaptation of the deployed defenses. To our knowledge, this is the first work to: (i) provide a joint model of malware and its detection, (ii) provide a model that is complete with respect to all implementable malware and detection approaches, (iii) provide a formal bridge between Turing-reducibility and measure theory, and (iv) thereby, allow game theory's strict dominance solution concept to be applied to formally reason about the requirements if the malware versus anti-malware arms-race is to converge to a defender advantageous end-game. / Graduate / melgamal@uvic.ca

In this thesis a new solution concept is developed for n-player, nonzero sum games. The solution concept is based in reinterpreting the n-player nonzero sum game into 2-player zero sum games. The n-player nonzero sum game is first rewritten as an n + 1 player coalition game. The definition of zero sum payment is that one player pays the other what he gets in a given outcome (coalition of the n + 1 player game). Who pays whom depends on the coalition. More than one 2-player zero sum interpretation game always results from the procedure, and criteria are established to select one of the zero sum interpretation games. The solution concept defines results identical to the minimax concept when applied directly to zero sum 2-player games. When applied to 2-player prisoner’s dilemma games, the solution procedure assigns mixed strategies to the prisoners, thereby “resolving” the dilemma. The mixed strategies vary with the payoffs (up to a linear transformation). For prisoner’s dilemma matrices which have been used in large numbers of gaming experiments, the solution concept predicts dynamically, i.e., by play number, the “fraction of cooperative choices” for (approximately) the first 30 plays. In addition, the mixed strategy appears in a game between each subject (prisoner) and the n + 1st player (district attorney), suggesting that the subjects have been playing against the experimenter. Empirical evidence for this conclusion is given. A theorem is proved for n-player prisoner’s dilemma games. Game theory is reviewed to show the roots of this solution concept in the heuristic use of zero sum n-player games in the von Neumann and Morgenstern theory, and in rational decision making models, e.g., “games against Nature.” The empirical and formal difficulties of the equilibrium point solution concept for nonzero sum games are discussed. Detailed connections between game theory and cybernetics are discussed.

In this thesis, we introduce a non-probabilistic model for the short-term interest rate. The key concepts involved in this new approach are the non-diffusive nature of the short rate process and the uncertainty in the model parameters. The model assumes the worst possible outcome for the short rate path when pricing a fixed-income product (from the point of view of the holder) and differs in many important ways from the traditional approaches of fully deterministic or stochastic rates. In this new model, delta hedging and unique pricing play no role, nor does any market price of risk term appear. We present the model and explore the analytical and numerical solutions of the associated partial differential equation. We show how to optimally hedge the interest rate risk of a fixed-income portfolio and price and hedge common and exotic fixed-income products. Finally, we consider extensions to the model and present conclusions and areas for further research.

This thesis is concerned with the validation of chess endgame strategies. It is also concerned with the synthesis of strategies that can be validated. A strategy for a given player is the specification of the move to be made by that player from any position that may occur. This move may be dependent on the previous moves of both sides. A strategy is said to be correct if following the strategy always leads to an outcome of at least the same game theoretic value as the starting position. We are not concerned with proving the correctness of programs that implement the strategies under consideration. We shall be working with knowledge-based programs which produce playing strategies, and assume that their concrete implementations (in POP2, PROLOG etc.) are correct. The synthesis approach taken attempts to use the large body of heuristic knowledge and theory, accumulated over the centuries by chessmasters, to find playing strategies. Our concern here is to produce structures for representing a chessmaster's knowledge wnich can be analysed within a game theoretic model. The validation approach taken is that a theory of the domain in the form of the game theoretic model of chess provides an objective measure of the strategy followed by a program. Our concern here is to analyse the structures created in the synthesis phase. This is an instance of a general problem, that of quantifying the performance of computing systems. In general to quantify the performance of a system we need,- A theory of the domain. - A specification of the problem to be solved. - Algorithms and/or domain-specific knowledge to be applied to solve the problem.