Game Theoretic Models

Today, cyber deception is an essential element of proactive and reactive defense systems. Military equipment is increasingly connected to the Internet, and next-generation network security is becoming increasingly complex. This vast network of military tactics thus becomes exposed and vulnerable, opening a battlefield against cyberattacks. The security of such a network is therefore paramount. In this paper, we propose a game-theoretic cyber deception approach for the allocation of diversified honeypots. To ensure high availability of our security measure, we opt for diversified redundancy in honeypot placement. We will analyze the impact on the defender's reward, with or without the attacker's success probability. The optimization will be solved using the Nash balance. Regarding honeypot placement, we adopt a deployment with redundancy to guarantee the security of critical nodes. Our results show that redundancy strengthens the ability to diversify the honeypot, thus ensuring long-term network security.

Today, cyber deception is an essential element of proactive and reactive defense systems. Military equipment is increasingly connected to the Internet, and next-generation network security is becoming increasingly complex. This vast network of military tactics thus becomes exposed and vulnerable, opening a battlefield against cyberattacks. The security of such a network is therefore paramount. In this paper, we propose a game-theoretic cyber deception approach for the allocation of diversified honeypots. To ensure high availability of our security measure, we opt for diversified redundancy in honeypot placement. We will analyze the impact on the defender's reward, with or without the attacker's success probability. The optimization will be solved using the Nash balance. Regarding honeypot placement, we adopt a deployment with redundancy to guarantee the security of critical nodes. Our results show that redundancy strengthens the ability to diversify the honeypot, thus ensuring long-term network security.

In many Internet of Thing (IoT) application domains security is a critical requirement, because malicious parties can undermine the effectiveness of IoT-based systems by compromising single components and/or communication channels. Thus, a security infrastructure is needed to ensure the proper functioning of such systems even under attack. However, it is also critical that security be at a reasonable resource and energy cost. In this article, we focus on the problem of efficiently and effectively securing IoT networks by carefully allocating security resources in the network area. In particular, given a set of security resources R and a set of attacks to be faced A, our method chooses the subset of R that best addresses the attacks in A, and the set of locations where to place them, that ensure the security coverage of all IoT devices at minimum cost and energy consumption. We model our problem according to game theory and provide a Pareto-optimal solution in which the cost of the security infrastructure, its energy consumption, and the probability of a successful attack are minimized. Our experimental evaluation shows that our technique improves the system robustness in terms of packet delivery rate for different network topologies. Furthermore, we also provide a method for handling the computation of the resource allocation plan for large-scale networks scenarios, where the optimization problem may require an unreasonable amount of time to be solved. We show how our proposed method drastically reduces the computing time, while providing a reasonable approximation of the optimal solution. CCS Concepts: • Security and privacy → Formal security models; Distributed systems security; Mobile and wireless security;

A "noisy-rich" (NR) cyber-attacker (Lippmann et al. 2012) is one who tries all available vulnerabilities until he or she successfully compromises the targeted network. We develop an adversarial foundation, based on Stackelberg games, for how NR-attackers will explore an enterprise network and how they will attack it, based on the concept of a system vulnerability dependency graph. We develop a mechanism by which the network can be modified by the defender to induce deception by placing honey nodes and apparent vulnerabilities into the network to minimize the expected impact of the NR-attacker's attacks (according to multiple measures of impact). We also consider the case where the adversary learns from blocked attacks using reinforcement learning. We run detailed experiments with real network data (but with simulated attack data) and show that Stackelberg Honey-based Adversarial Reasoning Engine performs very well, even when the adversary deviates from the initial assumptions made about his or her behavior. We also develop a method for the attacker to use reinforcement learning when his or her activities are stopped by the defender. We propose two stopping policies for the defender: Stop Upon Detection allows the attacker to learn about the defender's strategy and (according to our experiments) leads to significant damage in the long run, whereas Stop After Delay allows the defender to introduce greater uncertainty into the attacker, leading to better defendability in the long run.

• Two treatments (anonymous conditions)-Static network (played prisoner's dilemma only)-Dynamic network (prisoner's dilemma followed by a link-breaking stage) • 10 groups per treatment of 10 individuals (♀=55, ♂=45) • Starting network structure (Fig. 1A) • Duration of game unknown (30 rounds) • For each partner, participants decided whether to cooperate (C) or defect (D) (cf. payoff matrix) • Dynamic network groups: players can end their relationships & receive new partners, randomly chosen (cf. Fig. 1B) partner C D focus player C 0.25 €-0.10 € D 0.40 € 0.00 €

Adoption of dispersed renewable energy technologies requires transmission network expansion. Besides the transmission system operator (TSO), restructuring of electricity industries has introduced a merchant investor (MI), who earns congestion rents from constructing new lines. We compare these two market designs via a stochastic bi-level programming model that has either the MI or the TSO making transmission investment decisions at the upper level and power producers determining generation investment and operation at the lower level while facing wind power variability. We find that social welfare is always higher under the TSO because the MI has incentive to boost congestion rents by restricting capacities of transmission lines. Such strategic behavior also limits investment in wind power by producers. However, regardless of the market design (MI or TSO), when producers behave à la Cournot, a higher proportion of energy is produced by wind. In effect, withholding of generation capacity by producers prompts more transmission investment since the TSO aims to increase welfare by subsidizing wind and the MI creates more flow to maximize profit.

Wireless Sensor Networks typically consist of a large number of sensor nodes with constrained resources. Cluster-based routing algorithms for WSNs try to preserve battery power by grouping nodes into multiple clusters: a single node in each cluster, the Cluster Head (CH), communicates with a Base Station on behalf of the others. In an ideal collaborative setting, sensor nodes should alternate in the role of CH. However, the cooperation of nodes is not granted in WSNs with more than one governing authority, where sensor nodes can behave selfishly, in order to save their own resources. In this paper, we propose a novel evolutionary cluster-head determination algorithm called GREET, based on an Evolutionary Game Theory (EGT) approach. In the proposed algorithm, individual nodes adapt their strategies on the basis of the outcomes of the interactions with other nodes and converge to an Evolutionary Stable Strategy (ESS) equilibrium. We show that this ESS corresponds to one of the desired behavioral outcomes. This outcome is obtained without the support of external cooperation enforcement mechanisms. In the study, we use an analytic model of the population evolution, based on the so-called replicator dynamics, as a guide in the choice of the mechanisms, then we adapt the approach to realistic more scenarios. We show, by means of a systematic simulation study, that the algorithm extends the network lifetime and provides a better packet throughput, w.r.t other standard WSN algorithms, such as LEACH and CROSS.

Reconnaissance activities precedent other attack steps in the cyber kill chain. Zero-day attacks exploit unknown vulnerabilities and give attackers the upper hand against conventional defenses. Honeypots have been used to deceive attackers by misrepresenting the true state of the network. Existing work on cyber deception does not model zero-day attacks. In this paper, we address the question of "How to allocate honeypots over the network?" to protect its most valuable assets. To this end, we develop a two-player zero-sum game theoretic approach to study the potential reconnaissance tracks and attack paths that attackers may use. However, zero-day attacks allow attackers to avoid placed honeypots by creating new attack paths. Therefore, we introduce a sensitivity analysis to investigate the impact of different zero-day vulnerabilities on the performance of the proposed deception technique. Next, we propose several mitigating strategies to defend the network against zero-day attacks based on this analysis. Finally, our numerical results validate our findings and illustrate the effectiveness of the proposed defense approach.

This paper proposes a game theoretic methodology for analysis and design of survivable and secure systems and protocols. Modern game theory provides a natural setup for dealing with adversarial situations, which may involve multiple adversaries with different objectives, resources, access and risk tolerance, e.g., hackers, terrorists, and national intelligence organizations. The solution based on the game theoretic methodology balances ease of access and cost-effectiveness on the one hand, with survivability, fault tolerance, and security on the other hand. The main methodological difficulty in developing game theoretic model of real systems is quantifications of the "rules of the game", including set of feasible strategies and utility function for each player. Computational challenges result from difficulty of solving games with realistic number of players and feasible strategies.

Over the last two decades, the electricity industry has shifted from regulation of monopolistic and centralized utilities towards deregulation and promoted competition. With increased competition in electric power markets, system operators are recognizing their pivotal role in ensuring the efficient operation of the electric grid and the maximization of social welfare. In this article, we propose a hypothetical new market of dynamic spatial network equilibrium among consumers, system operators and electricity generators as the solution of a dynamic Stackelberg game. In that game, generators form an oligopoly and act as Cournot-Nash competitors who non-cooperatively maximize their own profits. The market monitor attempts to increase social welfare by intelligently employing equilibrium congestion pricing anticipating the actions of generators. The market monitor influences the generators by charging network access fees that influence power flows towards a perfectly competitive scenario. Our approach anticipates uncompetitive behavior and minimizes the impacts upon society. The resulting game is modeled as a Mathematical Program with Equilibrium Constraints (MPEC). We present an illustrative example as well as a stylized 15-node network of the Western European electric grid.

Over the last two decades, the electricity industry has shifted from regulation of monopolistic and centralized utilities towards deregulation and promoted competition. With increased competition in electric power markets, system operators are recognizing their pivotal role in ensuring the efficient operation of the electric grid and the maximization of social welfare. In this article, we propose a hypothetical new market of dynamic spatial network equilibrium among consumers, system operators and electricity generators as the solution of a dynamic Stackelberg game. In that game, generators form an oligopoly and act as Cournot-Nash competitors who non-cooperatively maximize their own profits. The market monitor attempts to increase social welfare by intelligently employing equilibrium congestion pricing anticipating the actions of generators. The market monitor influences the generators by charging network access fees that influence power flows towards a perfectly competitive scenario. Our approach anticipates uncompetitive behavior and minimizes the impacts upon society. The resulting game is modeled as a Mathematical Program with Equilibrium Constraints (MPEC). We present an illustrative example as well as a stylized 15-node network of the Western European electric grid.

An oligopoly with spatially dispersed generators and consumers and with multi-period demand is modeled in this paper. The producers are assumed to behave in a Cournot manner with regulated transmission prices. A (generalized) Nash equilibrium is sought. The story of the game is as follows. Each generator takes its rivals' output (generation, supply, and flows) and the prices for transmission services as fixed when it decides upon its output to maximize its profit; the transmission firm takes the quantities of transmission services demanded by the generators as fixed when it determines the transmission prices according to certain regulatory rules. An equilibrium of the model is a set of generation output at which no generator will obtain more profit if it unilaterally modifies its output from this set, and a set of transmission prices satisfying certain regulatory requirements. A variational inequality approach is used for computing the equilibria of the model. Using the same app...

The Internet, with the rise of the IoT, is one of the most powerful means of propagating a terrorist threat, and at the same time the perfect environment for deploying ubiquitous online surveillance systems.This paper tackles the problem of online surveillance, which we define as the monitoring by a security agency of a set of websites through tracking and classification of profiles that are potentially suspected of carrying out terrorist attacks. We conduct a theoretical analysis in this scenario that investigates the introduction of automatic classification technology compared to the status quo involving manual investigation of the collected profiles. Our analysis starts examining the suitability of game-theoretic-based models for decision-making in the introduction of this technology. We propose an adversarial-risk-analysis (ARA) model as a novel way of approaching the online surveillance problem that has the advantage of discarding the hypothesis of common knowledge. The propose...

Defensive deception is a promising approach for cyber defense. Via defensive deception, the defender can anticipate attacker actions; it can mislead or lure attacker, or hide real resources. Although defensive deception is increasingly popular in the research community, there has not been a systematic investigation of its key components, the underlying principles, and its tradeoffs in various problem settings. This survey paper focuses on defensive deception research centered on game theory and machine learning, since these are prominent families of artificial intelligence approaches that are widely employed in defensive deception. This paper brings forth insights, lessons, and limitations from prior work. It closes with an outline of some research directions to tackle major gaps in current defensive deception research.

Defensive deception techniques have emerged as a promising proactive defense mechanism to mislead an attacker and thereby achieve attack failure. However, most game-theoretic defensive deception approaches have assumed that players maintain consistent views under uncertainty. They do not consider players’ possible, subjective beliefs formed due to asymmetric information given to them. In this work, we formulate a hypergame between an attacker and a defender where they can interpret the same game differently and accordingly choose their best strategy based on their respective beliefs. This gives a chance for defensive deception strategies to manipulate an attacker’s belief, which is the key to the attacker’s decision making. We consider advanced persistent threat (APT) attacks, which perform multiple attacks in the stages of the cyber kill chain where both the attacker and the defender aim to select optimal strategies based on their beliefs. Through extensive simulation experiments, ...

Cyber deception techniques are crucial to protect networks in battlefield settings and combat malicious cyber attacks. Cyber deception can effectively disrupt the surveillance process outcome of an adversary. In this paper, we propose a novel approach for cyber deception to protect important nodes and trap the adversary. We present a sequential approach of honeypot placement to defend and protect the network vital nodes. We formulate a stochastic game to study the dynamic interactions between the network administrator and the attacker. The defender makes strategic decisions about where to place honeypots to introduce new vulnerabilities to the network. The attacker's goal is to develop an attack strategy to compromise the nodes of the network by exploiting a set of known vulnerabilities. To consider a practical threat model, we assume that the attacker can only observe a noisy version of the network state. To this end, both players solve a partially observable stochastic game (POSG). Finally, we present a discussion on existing techniques to solve the formulated game and possible approaches to reduce the game complexity as part of our ongoing and future research. CCS CONCEPTS • Networks → Network security; Mobile and wireless security.

In a multi-tenant cloud, a number of Virtual Machines (VMs) are collocated on the same physical machine to optimize performance, power consumption and maximize profit. This, however, increases the risk of a malicious VM performing side-channel attacks and leaking sensitive information from neighboring VMs. To this end, this paper develops and analyzes a game-theoretic framework for the VM migration timing problem in which the cloud provider decides when to migrate a VM to a different physical machine to reduce the risk of being compromised by a collocated malicious VM. The adversary decides the rate at which she launches new VMs to collocate with the victim VMs. Our formulation captures a data leakage model in which the cost incurred by the cloud provider depends on the duration of collocation with malicious VMs. It also captures costs incurred by the adversary in launching new VMs and by the defender in migrating VMs. We establish sufficient conditions for the existence of Nash equilibria for general cost functions, as well as for specific instantiations, and characterize the best response for both players. Furthermore, we extend our model to characterize its impact on the attacker's payoff when the cloud utilizes intrusion detection systems that detect side-channel attacks. Our theoretical findings are corroborated with extensive numerical results in various settings.

We present a structural framework to describe games in terms of components. The components are divided into four major areas: meta-structure, bounding, narrative and objective. The framework is developed to be used in conjunction with game design patterns, descriptions of patterns of interaction relevant to game play. We describe the development of the framework and how it relates to patterns.

Game Theory (GT) has been used with significant success to formulate, and either design or optimize, the operation of many representative communications and networking scenarios. The games in these scenarios involve, as usual, diverse players with conflicting goals. This paper primarily surveys the literature that has applied theoretical games to wireless networks, emphasizing use cases of upcoming Multi-Access Edge Computing (MEC). MEC is relatively new and offers cloud services at the network periphery, aiming to reduce service latency backhaul load, and enhance relevant operational aspects such as Quality of Experience or security. Our presentation of GT is focused on the major challenges imposed by MEC services over the wireless resources. The survey is divided into classical and evolutionary games. Then, our discussion proceeds to more specific aspects which have a considerable impact on the game's usefulness, namely: rational vs. evolving strategies, cooperation among players, available game information, the way the game is played (single turn, repeated), the game's model evaluation, and how the model results can be applied for both optimizing resource-constrained resources and balancing diverse trade-offs in real edge networking scenarios. Finally, we reflect on lessons learned, highlighting future trends and research directions for applying theoretical model games in upcoming MEC services, considering both network design issues and usage scenarios.

Most security defenses can be breached by motivated adversaries, therefore in addition to attack-prevention technologies, rms investing in cyber-security for their information technology infrastructure need to consider attack-detection and restoration tools to detect intruders, and restore their system to a safe condition. Attackers face similar investment alternatives; they need to invest resources to nding vulnerabilities in a protected system, and once the protection has been broken, they need to invest in the infrastructure necessary to exploit these attacks and maintain stealthy persistence in the compromised infrastructure. We model this dual considerations as a dynamic programming problem between attackers and defenders and then study the Nash equilibrium of this game. Our goal is to nd models and alternatives that can help us understand optimal security investments in prevention and detection against advanced rational adversaries.

Internet of Things (IoT) devices and applications can have significant vulnerabilities, which may be exploited by adversaries to cause considerable harm. An important approach for mitigating this threat is remote attestation, which enables the defender to remotely verify the integrity of devices and their software. There are a number of approaches for remote attestation, and each has its unique advantages and disadvantages in terms of detection accuracy and computational cost. Further, an attestation method may be applied in multiple ways, such as various levels of software coverage. Therefore, to minimize both security risks and computational overhead, defenders need to decide strategically which attestation methods to apply and how to apply them, depending on the characteristic of the devices and the potential losses. To answer these questions, we first develop a testbed for remote attestation of IoT devices, which enables us to measure the detection accuracy and performance overhead of various attestation methods. Our testbed integrates two example IoT applications, memory-checksum based attestation, and a variety of software vulnerabilities that allow adversaries to inject arbitrary code into running applications. Second, we model the problem of finding an optimal strategy for applying remote attestation as a Stackelberg security game between a defender and an adversary. We characterize the defender's optimal attestation strategy in a variety of special cases. Finally, building on experimental results from our testbed, we evaluate our model and show that optimal strategic attestation can lead to significantly lower losses than naïve baseline strategies.

Wireless Sensor Networks (WSNs) are made up of small low-power nodes that are used in different areas like environment monitoring and several military and civilian applications. But due to its small size and limited energy source, energy efficiency is its main area of concern, and many methods have been developed to improve its network lifetime. Game Theory is being used in WSNs to improve the energy efficiency of a network and its lifetime. Game Theory is suitable for such problems as it can be used in node or network level to encourage the decision-making capabilities of WSNs. This survey paper focuses on different types of clustering protocols designed in WSNs using Game Theory to combat the problem of energy efficiency. In particular, we address the approaches by which Game Theory has been used in WSNs to improve its network lifetime including the games used in each protocol.

Security systems for email spam filtering, network intrusion detection, steganalysis, and watermarking, frequently use classifiers to separate malicious behavior from legitimate. Typically, they use a fixed operating point minimizing the expected cost / error. This allows a rational attacker to deliver invisible attacks just below the detection threshold. We model this situation as a non-zero sum normal form game capturing attacker's expected payoffs for detected and undetected attacks, and detector's costs for false positives and false negatives computed based on the Receiver Operating Characteristic (ROC) curve of the classifier. The analysis of Nash and Stackelberg equilibria reveals that using a randomized strategy over multiple operating points forces the rational attacker to design less efficient attacks and substantially lowers the expected cost of the detector. We present the equilibrium strategies for sample ROC curves from network intrusion detection system and evaluate the corresponding benefits.

Before executing an attack, adversaries usually explore the victim's network in an attempt to infer the network topology and identify vulnerabilities in the victim's servers and personal computers. Falsifying the information collected by the adversary post penetration may significantly slower lateral movement and increase the amount of noise generated within the victim's network. We investigate the effect of fake vulnerabilities within a real enterprise network on the attacker performance. We use the attack graphs to model the path of an attacker making its way towards a target in a given network. We use combinatorial optimization in order to find the optimal assignments of fake vulnerabilities. We demonstrate the feasibility of our deception-based defense by presenting results of experiments with a large scale real network. We show that adding fake vulnerabilities forces the adversary to invest a significant amount of effort, in terms of time and exploitability cost.

Rink hockey like any other team sport game is necessary to have a mastery of the game object "the ball" and for this it is necessary that athletes do not have a deficient training steps in that process, because it is during their development, that the athlete is in an optimal situation to improve on this aspect of the game. The study objective was to identify which game model allows young athletes to have a greater number of interactions with the ball in rink hockey in the levels of “Under 8” and “Under 10”. Our study sample consisted of 24 games: 8 games in the 3x3 model; 8 games in the 4x4 model and 8 games in the 5x5 model. The 24 matches are equally divided by the levels of Under 8 and Under 10 with a total of 88 children participating. For data collection instrument we used the deferred observation method where we proceeded to a descriptive analysis of the data to find the means, standard deviation and totals of: Shots, Passes, Interceptions and Goals. The Mann-Whitne...

In this paper we present a novel Stackelberg-type model of security domains: Security Assets aSsignment with Information disclosure (SASI). The model combines both the features of the Stackelberg Security Games (SSGs) model and of the Bayesian Persuasion (BP) model. More specifically, SASI includes: a) an uncontrolled, exogenous security state that serves as the Defender's private information; b) multiple security assets with non-accumulating, targetlocal defence capability; c) a pro-active, verifiable and public, unidirectional information disclosure channel from the Defender to the Attacker. We show that SASI with a non-degenerate information disclosure can be arbitrarily more efficient, than a "silent" Stackelberg assets allocation. We also provide a linear program reformulation of SASI that can be solved in polynomial time in SASI parameters. Furthermore, we show that it is possible to remove one of SASI parameters and, rather than require it as an input, recover it by computation. As a result, SASI becomes highly scalable.

Abstract— Because of the stochastic nature of traffic requirement matrix, it's very difficult to get the optimal traffic distribution to minimize the delay even with adaptive routing protocol in a fixed connection network where capacity already defined for each link. Hence there is a ...

A “noisy-rich” (NR) cyber-attacker (Lippmann et al. 2012) is one who tries all available vulnerabilities until he or she successfully compromises the targeted network. We develop an adversarial foundation, based on Stackelberg games, for how NR-attackers will explore an enterprise network and how they will attack it, based on the concept of a system vulnerability dependency graph. We develop a mechanism by which the network can be modified by the defender to induce deception by placing honey nodes and apparent vulnerabilities into the network to minimize the expected impact of the NR-attacker’s attacks (according to multiple measures of impact). We also consider the case where the adversary learns from blocked attacks using reinforcement learning. We run detailed experiments with real network data (but with simulated attack data) and show that Stackelberg Honey-based Adversarial Reasoning Engine performs very well, even when the adversary deviates from the initial assumptions made a...

Most past work on honeypots has made two assumptions: (i) they assume that the only defensive measure used is a honeypot mechanism, and (ii) they do not consider both rational and subrational adversaries and do not reason with an adversary model when placing honeypots. However, real-world system security officers use a mix of instruments such as traditional defenses (e.g. firewalls, intrusion detection systems), and honeypots form only one portion of the strategy. Moreover, the placement of traditional defenses and honeypots cannot be done independently. In this paper, we consider a Stackelbergstyle game situation where the defender models the attacker and uses that model to identify the best placement of traditional defenses and honeypots. We provide a formal definition of undamaged asset value (i.e. the value that is not compromised by the attacker) under a given defensive strategy and show that the problem of finding the best placement so as to maximize undamaged asset value is NP-hard. We propose a greedy algorithm and show via experiments, both on real enterprise networks and on ones generated by the well-known network simulation tool NS-2, that our algorithm quickly computes near optimal placements. As such, our method is both practical and effective.

Abstract: Clustering real world data often faced with curse of dimensionality, where real world data often consist of many dimensions. Multidimensional data clustering evaluation can be done through a density-based approach. Density approaches based on the paradigm introduced by DBSCAN clustering. In this approach, density of each object neighbours with MinPoints will be calculated. Cluster change will occur in accordance with changes in density of each object neighbours. The neighbours of each object typically ...

This paper lays the foundation for constructing a comprehensive analytical tool for selection of managers on the basis of core leadership capabilities. A game theoretic model, where the key coefficient of the input vector was inferred from the twelve variables of the April² Leadership Enhancement Framework, using a set of structural equations, was adopted to reach the goals of the study. The underlying data for estimation of the coefficient was sampled from the population of graduate management students from a leading South African business school using Likert scale questionnaires. The paper finds highly significant predictive relationships between the key variables of the April² Leadership Enhancement Framework, confirming the theoretical claim of the model that managers with higher levels of development of these twelve characteristics can be regarded as having greater leadership capability. The structural equations revealed that the key coefficient of the main input vector of the game, despite having highly significant explanatory power over the underlying variables, is not generalizable. Further research should focus on improvement of the fit, by sampling from wider populations in order to make the tool applicable across the board. An important implication of the constructed model is that the signalling game of incomplete information, combined with key variables of the Leadership Enhancement Framework, serves as a robust foundation for a further development of an analytical tool for a structured selection of managers on the basis of their core leadership capabilities. It is also a first known attempt to combine psychometric techniques with a game theoretic framework in the context of leadership development.

Game Theory (GT) has been used with outstanding outcomes to formulate, and either design or optimize, the operation of a huge number of representative communications and networking scenarios, each one with distinct entities aiming at different and usually contradictory goals. This paper comprehensively surveys and updates, in a tutorial style, the literature contributions that have applied a diverse set of theoretical games to wireless networks, emphasizing scenarios of upcoming Mobile Edge Computing (MEC). MEC is a promising model that aims to offer cloud services at the network periphery, reducing service latency and backhaul load, and enhancing Quality of Experience. Our literature discussion of GT is structured according to a taxonomy that has three top-level groups: classical, evolutionary, and incomplete information. Then, the above groups are divided considering pertinent game aspects that have a great impact on players’ final decisions, namely: rational vs. evolving strategi...

Game Theory (GT) has been used with significant success to formulate, and either design or optimize, the operation of many representative communications and networking scenarios. The games in these scenarios involve, as usual, diverse players with conflicting goals. This paper primarily surveys the literature that has applied theoretical games to wireless networks, emphasizing use cases of upcoming Multi-Access Edge Computing (MEC). MEC is relatively new and offers cloud services at the network periphery, aiming to reduce service latency backhaul load, and enhance relevant operational aspects such as Quality of Experience or security. Our presentation of GT is focused on the major challenges imposed by MEC services over the wireless resources. The survey is divided into classical and evolutionary games. Then, our discussion proceeds to more specific aspects which have a considerable impact on the game's usefulness, namely: rational vs. evolving strategies, cooperation among players, available game information, the way the game is played (single turn, repeated), the game's model evaluation, and how the model results can be applied for both optimizing resource-constrained resources and balancing diverse trade-offs in real edge networking scenarios. Finally, we reflect on lessons learned, highlighting future trends and research directions for applying theoretical model games in upcoming MEC services, considering both network design issues and usage scenarios.

We study the problem of optimal resource allocation for packet selection and inspection to detect potential threats in large computer networks with multiple computers of differing importance. An attacker tries to harm these targets by sending malicious packets from multiple entry points of the network; the defender thus needs to optimally allocate her resources to maximize the probability of malicious packet detection under network latency constraints. We formulate the problem as a graph-based security game with multiple resources of heterogeneous capabilities and propose a mathematical program for finding optimal solutions. We also propose Grande, a novel polynomial time algorithm that uses an approximated utility function to circumvent the limited scalability caused by the attacker's large strategy space and the non-linearity of the aforementioned mathematical program. Grande computes solutions with bounded error and scales up to problems of realistic sizes.

We study the problem of optimal resource allocation for packet selection and inspection to detect potential threats in large computer networks with multiple computers of differing importance. An attacker tries to harm these targets by sending malicious packets from multiple entry points of the network; the defender thus needs to optimally allocate her resources to maximize the probability of malicious packet detection under network latency constraints. We formulate the problem as a graph-based security game with multiple resources of heterogeneous capabilities and propose a mathematical program for finding optimal solutions. We also propose Grande, a novel polynomial time algorithm that uses an approximated utility function to circumvent the limited scalability caused by the attacker's large strategy space and the non-linearity of the aforementioned mathematical program. Grande computes solutions with bounded error and scales up to problems of realistic sizes.

Kutlu (2009, “Price discrimination in Stackelberg competition”, Journal of Industrial Economics) shows that the Stackelberg leader sells to the highest value consumers and only the Stackelberg follower practises price discrimination. We show that this result is not robust if the marginal cost of the leader is lower than that of the follower. In this situation, both the leader and the follower practise price discrimination.

Partially owing to the political deadlock in Washington DC, US climate policy, historically, has been driven mainly by state or regional effort, such as the Regional Greenhouse Gas Initiative in the northeast United States and California AB 32. One major change recently is the introduction of the Clean Power Plan (CPP). CPP is a new federal-level policy introduced by the US Environmental Protection Agency to cut CO 2 emissions from existing fossil-fuel power plants by 30% below 2005 levels by 2030. While the proposal establishes a state-specific target with various "building blocks" that lay out possible reduction strategies, it leaves states and the power sector with considerable flexibility as for how to achieve their goals. More specifically, a state can decide to adopt either 1) a default "rate-based" standard where tons of CO 2 emissions per megawatt hour of electricity generated is measured, or 2) an equivalent "mass-based" standard, such as in a cap-and-trade (C&T) regime based on their projection of GDP growth. Economic theory suggests that the two approaches would provide incentives that might alter a firm's production decisions in a very different way (Bushnell et al, 2014). Furthermore, whereas the regulatory body at the state level as well as industry might value the "flexibility" to a great extent, the fact that the territory of a regional power/electric market, such as PJM (Pennsylvania-Jersey-Maryland) or NEISO (New England Independent System Operator), typically goes beyond the state boundary and encompasses a number of states makes it challenging to evaluate the effectiveness of the policy. One emerging issue that has received little attention is the possibility of strategic behavior in rate-or performancebased standards as well as its repercussions or spillovers to the product market. The consequences of market power can include price distortions, production inefficiencies, and a redistribution of income from consumers to suppliers. In fact, the distribution of economic rent or welfare analysis needs further attention when comparing performace-to mass-based policies. In particular, while the government collects all the proceedings when tradable permits are auctioned off in initial allocations, the rate-based policy is inherently revenue neutral. This is mainly because, similar to a renewable portfolio standard, the extent to which a generator needs to pay when complying with the policy depends on its emission rate relative to the performance standard. In the case where its emission rate is greater than the performance standard, it will need to pay an emission cost to cover its emissions, effectively elevating its marginal cost of production. On the other hand, when a generator's emission rate is less than the performance standard, the negative cost becomes a subsidy that effectively lowers its production cost, thereby making the generator more competitive. Thus, it is unclear if one policy will outputform the other one on ecoconomic grounds, especially when concerning distributional effects.

We formulate a dynamic facility location model for a …rm locating on a discrete network. It is assumed that this locating …rm will act as the leader …rm in an industry characterized by Stackelberg leader-follower competition. The …rm's I competitors are assumed to act as Cournot …rms and are each assumed to operate under the assumption of zero conjectural variation with respect to their I 1 Cournot competitors. Using sensitivity analysis of variational inequalities within a hierachical mathematical programming approach, we develop reaction function based dynamic models to optimize the Stackelberg …rm's location decision. In the second half of this paper, we use these models to illustrate through a numerical example the enhanced insights yielded by a reaction based, dynamic approach

1 Device-to-Device (D2D) communication is expected to be a key feature supported by 5G networks, especially due to the proliferation of Mobile Edge Computing (MEC), which has a prominent role in reducing network stress by shifting computational tasks from the Internet to the mobile edge. Apart from being part of MEC, D2D can extend cellular coverage allowing users to communicate directly when telecommunication infrastructure is highly congested or absent. This significant departure from the typical cellular paradigm imposes the need for decentralised network routing protocols. Moreover, enhanced capabilities of mobile devices and D2D networking will likely result in proliferation of new malware types and epidemics. Although the literature is rich in terms of D2D routing protocols that enhance quality-of-service and energy consumption, they provide only basic security support, e.g., in the form

A Wireless Sensor Network (WSN) is a network of a small, lightweight, portable sensor nodes, which can sense features within its proximity e.g. temperature. A sensor node possesses limited power resources. Clustering is one of the several proposals investigated to prolong the WSN lifetime. In this work, we introduce clustering algorithms in multimodal WSN. A Multimodal network, in this context, means that each sensor node is capable of sensing more than one feature from the monitored field. Up to our knowledge, this is the first work to consider clustering in multimodal WSN. We utilized K-Means as a general clustering algorithm not designed especially for WSN and compared it to LEACH-C. Different variations of K-means are introduced for efficient clustering in multimodal WSNs. The extensive set of experiments shows that our proposed clustering algorithms outperform the LEACH-C, which is especially designed for WSN, under different problem settings.

1 Device-to-Device (D2D) communication is expected to be a key feature supported by 5G networks, especially due to the proliferation of Mobile Edge Computing (MEC), which has a prominent role in reducing network stress by shifting computational tasks from the Internet to the mobile edge. Apart from being part of MEC, D2D can extend cellular coverage allowing users to communicate directly when telecommunication infrastructure is highly congested or absent. This significant departure from the typical cellular paradigm imposes the need for decentralised network routing protocols. Moreover, enhanced capabilities of mobile devices and D2D networking will likely result in proliferation of new malware types and epidemics. Although the literature is rich in terms of D2D routing protocols that enhance quality-of-service and energy consumption, they provide only basic security support, e.g., in the form

1 Device-to-Device (D2D) communication is expected to be a key feature supported by 5G networks, especially due to the proliferation of Mobile Edge Computing (MEC), which has a prominent role in reducing network stress by shifting computational tasks from the Internet to the mobile edge. Apart from being part of MEC, D2D can extend cellular coverage allowing users to communicate directly when telecommunication infrastructure is highly congested or absent. This significant departure from the typical cellular paradigm imposes the need for decentralised network routing protocols. Moreover, enhanced capabilities of mobile devices and D2D networking will likely result in proliferation of new malware types and epidemics. Although the literature is rich in terms of D2D routing protocols that enhance quality-of-service and energy consumption, they provide only basic security support, e.g., in the form

In wireless sensor networks, energy conservation is one of the main issues since sensor nodes have limited energy source. Clustering came as an effort to reduce the energy consumption of sensor nodes. Besides energy conservation, the sensor nodes should also deliver their data to the sink effectively. In this paper, a cost-and reward-based clustering (CORE) algorithm using game theory is proposed. In the cluster head selection, CORE considers the cost and reward of being a cluster head. The proposed CORE gives a performance tradeoff between energy consumption and the amount of data sent to the sink. Note that the energy consumption determines network lifetime. As a result, CORE is between probability-based clustering and clustered routing of selfish sensors (CROSS) with regard to performance.

To elucidate the mechanisms for the evolution of cooperation has been a core problem in biology, economics and sociology. Evolutionary game theory has proven to be an efficient approach to investigate the problem by using models based on so-called social dilemmas. Spatial structure is indicated to have an important effect on the evolution of cooperation and has been intensively studied during recent years. From this perspective, we review our studies in evolutionary dynamics based on a repeated game with three strategies, 'always defect' (ALLD), 'tit-for-tat' (TFT), and 'always cooperate' (ALLC). With mathematical analysis and numerical simulations, the results show that cooperation can be promoted in spatially-structured populations. Cooperators prevail against defectors by forming stable clusters, which is called the `spatial selection'. Meanwhile, lattice structure also inhibits cooperation due to the advantage of being spiteful. Furthermore, simulations demonstrate that a slight enforcement of ALLC can only promote cooperation when there is weak network reciprocity, while the catalyst effect of TFT on cooperation is verified.

We present a comprehensive robust decision support framework with novel computational algorithms for decision makers in a non-cooperative sequential setup. Existing simulation based approaches such as Monte Carlo methods can be inefficient under certain conditions like in presence of a high number of decision alternatives and uncertain outcomes. Hence, we provide a novel augmented probability simulation alternative to solve non-cooperative sequential games. We cover approaches to approximate subgame perfect equilibria under common knowledge conditions, assess the robustness of such solutions and, finally, approximate adversarial risk analysis solutions when lacking common knowledge. The proposed approach can be especially beneficial in application domains such as cybersecurity and counter-terrorism.

This paper lays the foundation for constructing a comprehensive analytical tool for selection of managers on the basis of core leadership capabilities. A game-theoretic model, where the key coefficient of the input vector was inferred from the twelve variables of the April² Leadership Enhancement Framework, using a set of structural equations, was adopted to reach the goals of the study. The underlying data for estimation of the coefficient was sampled from the population of graduate management students from a leading South African business school using Likert scale questionnaires. The paper finds highly significant predictive relationships between the key variables of the April² Leadership Enhancement Framework, confirming the theoretical claim of the model that managers with higher levels of development of these twelve characteristics can be regarded as having greater leadership capability. The structural equations revealed that the key coefficient of the main input vector of the game, despite having highly significant explanatory power over the underlying variables, is not generalizable. Further research should focus on improvement of the fit, by sampling from wider populations in order to make the tool applicable across the board. An important implication of the constructed model is that the signalling game of incomplete information, combined with key variables of the Leadership Enhancement Framework, serves as a robust foundation for further development of an analytical tool for a structured selection of managers on the basis of their core leadership capabilities. It is also a first known attempt to combine psychometric techniques with a game-theoretic framework in the context of leadership development.

— We investigate the use of pricing mechanisms as a means to achieve a desired feedback control strategy among selfish agents. We study a hierarchical linear-quadratic game with many dynamically coupled Nash followers and an uncoupled leader. The leader influences the game by choosing the quadratic dependence on control actions for each follower's cost function. We show that determining whether the leader can establish the desired feedback control as a Nash equilibrium among the followers is a convex feasibility problem for the continuous-time infinite horizon, discrete-time infinite horizon, and discrete-time finite horizon settings, and we present several extensions to this main result. In particular, we discuss methods for ensuring that the total cost incurred due to the leader's pricing is as close as possible to a specified nominal cost, as well as methods for minimizing the explicit dependence of a player's cost on other players' control inputs. Finally, we apply the proposed method to the problem of ensuring the security of a multi-network and to the problem of pricing for controlled diffusion in a general network.

Wireless sensor networks, are always in danger of extinction, due to limits on the amount of energy (battery) their nodes, high consumption Energy in addition to the lack of interchangeability of batteries. When communicating network between nodes to the base station (data repository) and sending information the highest energy consumption is happen, that is, the base station limits on the amount of received messages and save them.. Now, we can increase lifetime these network (that are directly related to energy consumption) if we can reduce energy consumption and messages during transmission and storage. So to reduce energy consumption in wireless sensor networks, it is proposed algorithms and large models. In this study, we have tried to offer a new model for cooperation nodes within communicate with the base station and also reduce the number of messages sent by using the game theory and exclusively market entry.