Game theory for security: Key algorithmic principles, deployed systems, lessons learned

Security is a critical concern around the world. In many domains from counter-terrorism to sustainability, limited security resources prevent full security coverage at all times; instead, these limited resources must be scheduled, while simultaneously taking into account different target priorities, the responses of the adversaries to the security posture and potential uncertainty over adversary types.

Advances in computational game theory have led to several successfully deployed applications in security domains. These game-theoretic approaches and security applications learn game payoff values or adversary behaviors from annotated input data provided by domain experts and practitioners in the field, or collected through experiments with human subjects. Beyond these traditional methods, unmanned aerial vehicles (UAVs) have become an important surveillance tool used in security domains to collect the required annotated data. However, collecting annotated data from videos taken by UAVs efficiently, and using these data to build datasets that can be used for learning payoffs or adversary behaviors in game-theoretic approaches and security applications, is an under-explored research question. This paper presents VIOLA, a novel labeling application that includes (i) a workload distribution framework to efficiently gather human labels from videos in a secured manner; (ii) a software interface with features designed for labeling videos taken by UAVs in the domain of wildlife security. We also present the evolution of VIOLA and analyze how the changes made in the development process relate to the efficiency of labeling, including when seemingly obvious improvements surprisingly did not lead to increased efficiency. VIOLA enables collecting massive amounts of data with detailed information from challenging security videos such as those collected aboard UAVs for wildlife security. VIOLA will lead to the development of a new generation of game-theoretic approaches for security domains, including approaches that integrate deep learning and game theory for real-time detection and response.

In this paper, we focus on mechanism design for single leader Stackelberg problems, which are a special case of hierarchical decision making problems in which a distinguished agent, known as the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">leader</i> , makes the first move and this action is followed by the actions of the remaining agents, which are known as the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">followers</i> . These problems are also known as <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">single</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">leader</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rest</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">follower</i> (SLRF) problems. There are many examples of such problems in the areas of electronic commerce, supply chain management, manufacturing systems, distributed computing, transportation networks, and multiagent systems. The game induced among the agents for these problems is a Bayesian Stackelberg game, which is more general than a Bayesian game. For this reason, classical mechanism design, which is based on Bayesian games, cannot be applied as is for solving SLRF mechanism design problems. In this paper, we extend classical mechanism design theory to the specific setting of SLRF problems. As a significant application of the theory developed, we explore two examples from the domain of electronic commerce- <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">first-price</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">and</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">second-price</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">electronic</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">procurement</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">auctions</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">with</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reserve</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">prices</i> . Using an SLRF model for these auctions, we derive certain key results using the SLRF mechanism design framework developed in this paper. The theory developed has many promising applications in modeling and solving emerging game theoretic problems in engineering.

Traditional mobile crowdsensing has shown a great potential to address large-scale data sensing problems by allocating sensing tasks to pervasive mobile users with smart devices. As crowdsensing service provider involves data collection from mobile users, the issue of providing rewards to incentivize mobile users is fundamentally important to ensure the sustainability of crowdsensing system. Recently, socially-aware crowdsensing services have been introduced as the integration of social networks and crowdsensing platforms. For example, in health-related crowdsensing applications, a mobile user benefits from information regarding food, exercise, medicine and medical treatment collected and shared by her socially-connected friends and family members. In this thesis, incentive mechanism design is revisited in the context of socially-aware crowdsensing, where the game theory tool is used to study the interactions between crowdsensing service provider and mobile users. In the first part of the thesis, incentive mechanism is designed by considering the underlying social network effects amid mobile social networks, for motivating the participants. Namely, one MU will obtain additional benefits from information contributed or shared by local neighbors in social networks. The process of rewarding and participating is modeled as a two-stage game, and backward induction is used to analyze the mobile users' participation level and the crowdsensing service provider's optimal reward mechanism. The analytical expressions are derived for the discriminatory incentive as well as the uniform incentive mechanisms. In the second part of the thesis, information asymmetry challenges are investigated. A Bayesian Stackelberg game with incomplete information is formulated to analyze the interaction between the crowdsensing service provider and mobile users, where the social structure information, i.e., the social network effects, among mobile users is uncertain. The existence and uniqueness of the Bayesian Stackelberg equilibrium is analytically validated by identifying the best response strategies of the mobile users. To be more general and practical, different with the former two parts, incentive mechanism in presence of multiple crowdsensing service providers is studied in the third part of the thesis. Understanding the behaviors of mobile users and service providers in socially-aware crowdsensing is of paramount importance for incentive mechanisms. With this focus, a multi-leader and multi-follower Stackelberg game approach is proposed to model the strategic interactions among service providers and mobile users, where the social influence of mobile users and the strategic interconnections of service providers are jointly and formally integrated into the game modeling. Through backward induction methods, the existence and uniqueness of the Stackelberg equilibrium are theoretically proved. In summary, this thesis addresses a few urgent challenging problems with the tools of game theory in the real implementation of incentive mechanism design in the context of socially-aware crowdsensing. Extensive numerical simulations are also conducted to illustrate some important properties of the equilibrium. Finally, several promising research directions are outlined for the future work.

Game-theoretic approaches have been proposed for addressing the complex problem of assigning limited security resources to protect a critical set of targets. However, many of the standard assumptions fail to address human adversaries who security forces will likely face. To address this challenge, previous research has attempted to integrate models of human decision-making into the game-theoretic algorithms for security settings. The current leading approach, based on experimental evaluation, is derived from a well-founded solution concept known as quantal response and is known as BRQR. One critical difficulty with opponent modeling in general is that, in security domains, information about potential adversaries is often sparse or noisy and furthermore, the games themselves are highly complex and large in scale. Thus, we chose to examine a completely new approach to addressing human adversaries that avoids the complex task of modeling human decision-making. We leverage and modify robust optimization techniques to create a new type of optimization where the defender's loss for a potential deviation by the attacker is bounded by the distance of that deviation from the expected-value-maximizing strategy. To demonstrate the advantages of our approach, we introduce a systematic way to generate meaningful reward structures and compare our approach with BRQR in the most comprehensive investigation to date involving 104 security settings where previous work has tested only up to 10 security settings. Our experimental analysis reveals our approach performing as well as or outperforming BRQR in over 90% of the security settings tested and we demonstrate significant runtime benefits. These results are in favor of utilizing an approach based on robust optimization in these complex domains to avoid the difficulties of opponent modeling.

Traditional proactive network defenses deploy security resources in the network based on probabilistic policies to confuse potential attackers. However, this strategy can be exploited by stealthy attackers, leading to reduced efficiency and higher vulnerability. Game theory has been shown to provide a sound mathematical approach to overcome these deficiencies and determine an optimal defense strategy. However, existing game theoretic models typically either assume additive utility functions, or that the attacker can attack only one target. While such assumptions lead to tractable analyses, they miss key inherent dependencies that exist among different targets in current complex networks. In this chapter, we generalize the traditional security game model to the network scenario. We examine such a general security game from a theoretical perspective and provide a unified theoretical framework. In particular, we show that each security game is equivalent to a combinatorial optimization problem over a set system, which consists of defender’s pure strategy space. The key technique we use is based on projection of a polytope based transformation, and the ellipsoid method. We also provide several important applications of our developed framework, and show that for several problem classes, optimal defense strategies can be developed in polynomial time. Our approach paves the way for a deeper investigation into using game theoretic techniques for solving designing security mechanisms in networks, and we conclude by outlining a number of important future directions that need to be investigated.

Existing routing schemes in opportunistic networks can be classified two categories: multi-copy based schemes and single-copy based schemes. Multi-copy based schemes can increase the robustness of networks and reduce average delay. However, they consume a large amount of network resources and induce large overhead. Single-copy based schemes can save network resources, but it is difficult to guarantee the transmission delay and packet delivery ratio for these schemes. Hence, how to design an efficient routing scheme which can make a tradeoff between multi-copy schemes and single-copy schemes in opportunistic networks with limited resources is a challenging issue. Inspired by this issue, in this paper, we propose a novel routing scheme based on game theory in opportunistic networks under limited resources, named GTR. In GTR, we map the message transmission to a multi-player bargaining model. By constructing proper utility function, GTR can save the network resources, and achieve better performance in dense opportunistic networks. Our extensive simulation results also demonstrate this point, and GTR performs better compared to some classical routing schemes in terms of packet delivery ratio, average delay, overhead ratio and average hops.

Among the most fundamental tools for social network analysis are centrality measures, which quantify the importance of every node in the network. This centrality analysis typically disregards the possibility that the network may have been deliberately manipulated to mislead the analysis. To solve this problem, a recent study attempted to understand how a member of a social network could rewire the connections therein to avoid being identified as a leader of that network. However, the study was based on the assumption that the network analyzer-the seeker-is oblivious to any evasion attempts by the evader. In this paper, we relax this assumption by modelling the seeker and evader as strategic players in a Bayesian Stackelberg game. In this context, we study the complexity of various optimization problems, and analyze the equilibria of the game under different assumptions, thereby drawing the first conclusions in the literature regarding which centralities the seeker should use to maximize the chances of detecting a strategic evader.

In this paper, we consider the problem of traffic offloading in a two-tier downlink smallcell network with hybrid access. We formulate a Stackelberg game with pre-offloading decision, which includes two steps, to study the problem. The utility of the MBS is designed as a piecewise function, corresponding to the two possible decisions of the MBS. We design a bonus-based incentive mechanism to motivate smallcell base stations (SBSs) serve the MUEs. We prove the existence and uniqueness of the Stackelberg equilibrium. We also consider the incomplete information game, where the SBSs have private information. A Bayesian Stackelberg game with pre-offloading decision is formulated and the existence of Bayesian Stackelberg equilibrium is proved. Our proposed approach achieves much higher utilities than the existing approach does under some circumstances in both cases, which proves the necessity and superiority of our proposed game structure.

In this paper, a cyber-physical system (CPS) is considered, whose state estimation is done by a central controller (CC) using the measurements received from a wireless powered sensor network (WPSN) over fading channels. An adversary injects false data in this system by compromising some of the idle sensor nodes (SNs) of the WPSN. Using the WPSN for transmitting supervision and control data, in the aforementioned setting, makes the CPS vulnerable to both error and false data injection (FDI). The existing techniques of launching stealthy FDI attack are not applicable to the aforementioned network due to the random nature of wireless channels, which is used for both transmitting control and false data. The objectives of the adversary and the CC to launch stealthy FDI attack and to detect the same, respectively, are found to be depending on the powers they use for transmitting data over wireless channels. The transmit powers of the CC, and the adversary that fulfill their respective objectives are derived by modeling their interaction as a Bayesian Stackelberg game. Based on their objectives, novel utility functions are defined for the CC and the adversary. Subsequently, the equilibrium of the proposed game is obtained by solving a non-convex bi-level quadratic-quadratic program. Finally, the analytical results are verified and compared with other state-of-art techniques by applying them in a realistic smart grid simulations.

This paper utilizes a reconfigurable intelligent surface (RIS) to enhance the anti-jamming performance of wireless communications, due to its powerful capability of constructing smart and reconfigurable radio environment. In order to establish the practical interactions between the base station (BS) and the jammer, a Bayesian Stackelberg game is formulated, where the BS is the leader and the jammer acts as the follower. Specifically, with the help of a RIS-assisted transmitter, the BS attempts to reliably convey information to users with maximum utilities, whereas the smart jammer tries to interfere the signal reception of users with desired energy efficiency (EE) threshold. Since the BS and the jammer are not cooperative parties, the practical assumption that neither side can obtain the other&#x0027;s strategies is adopted in the proposed game, and the angular information based imperfect channel state information (CSI) is also considered. After tackling the practical assumption by using Cauchy-Schwarz inequality and the imperfect angular information by using the discretization method, the closed-form solution of both sides can be obtained via the duality optimization theory, which constitutes the unique Stackelberg equilibrium (SE). Numerical results demonstrate the superiority and validity of our proposed robust schemes over the existing approaches.

Protecting critical infrastructure is a challenging task for security agencies all around the world, which is exacerbated by terrorism. Many infrastructures have network characteristics, such as transportation and communication systems. Therefore, it is necessary to study them from the perspective of network science. Game theory provides a suitable theoretical tool for the study of attack-defense confrontation between rational agents. Sequential game, which is more in line with the actual situation, can better analyze the confrontation in infrastructure protection. Previous models which combine network science with game theory only consider the attacker as a single type. However, in the real world, accurate information about the type of the attacker is not always available for the defender. In this paper, we apply Bayesian Stackelberg game to model the attack-defense confrontation in the case of uncertain attacker type.

This dissertation presents novel approaches to modeling and analyzing security and resource allocation in mobile ad hoc networks (MANETs). The research involves the design, implementation and simulation of different models resulting in resource sharing and security’s strengthening of the network among mobile devices. Because of the mobility, the network topology may change quickly and unpredictably over time. Moreover, data-information sent from a source to a designated destination node, which is not nearby, has to route its information with the need of intermediary mobile nodes. However, not all intermediary nodes in the network are willing to participate in data-packet transfer of other nodes. The unwillingness to participate in data forwarding is because a node is built on limited resources such as energy-power and data. Due to their limited resource, nodes may not want to participate in the overall network objectives by forwarding data-packets of others in fear of depleting their energy power. To enforce cooperation among autonomous nodes, we design, implement and simulate new incentive mechanisms that used game theoretic concepts to analyze and model the strategic interactions among rationale nodes with conflicting interests. Since there is no central authority and the network is decentralized, to address the concerns of mobility of selfish nodes in MANETs, a model of security and trust relationship was designed and implemented to improve the impact of investment into trust mechanisms. A series of simulations was carried out that showed the strengthening of security in a network with selfish and malicious nodes. Our research involves bargaining for resources in a highly dynamic ad-hoc network. The design of a new arbitration mechanism for MANETs utilizes the Dirichlet distribution for fairness in allocating resources. Then, we investigated the problem of collusion nodes in mobile ad-hoc networks with an arbitrator. We model the collusion by having a group of nodes disrupting the bargaining process by not cooperating with the arbitrator. Finally, we investigated the resource allocation for a system between agility and recovery using the concept of Markov decision process. Simulation results showed that the proposed solutions may be helpful to decision-makers when allocating resources between separated teams.

Forest fires are serious disasters that endanger the safety and property of residents. To deal with the arson-caused forest fires, we use security game theory to generate optimal patrol strategy under limited patrolling resources. The existing security game algorithms focus on a single behavior model to prevent attacks, while our work is to incorporate the secondary damage caused by the attack behavior into the optimization object. For forest fires, the security game algorithm only expects to catch arsonists in the forest, which is unreasonable in our opinion. Our algorithm incorporates the spread of fires into patrolling path planning so that defenders avoid being swallowed by fire via capturing the spread of it.

Reasoning about knowledge—particularly the knowledge of agents who reason about the world and each other's knowledge—was once the exclusive province of philosophers and puzzle solvers. More recently, this type of reasoning has been shown to play a key role in a surprising number of contexts, from understanding conversations to the analysis of distributed computer algorithms. Reasoning About Knowledge is the first book to provide a general discussion of approaches to reasoning about knowledge and its applications to distributed systems, artificial intelligence, and game theory. It brings eight years of work by the authors into a cohesive framework for understanding and analyzing reasoning about knowledge that is intuitive, mathematically well founded, useful in practice, and widely applicable. The book is almost completely self-contained and should be accessible to readers in a variety of disciplines, including computer science, artificial intelligence, linguistics, philosophy, cognitive science, and game theory. Each chapter includes exercises and bibliographic notes. Bradford Books imprint