Proactive Network Defense with Game Theory

To step beyond the first-generation deployments of attacker-defender security games -- for LAX Police, US FAMS and others -- it is critical that we relax the assumption of perfect rationality of the human adversary. Indeed, this assumption is a well-accepted limitation of classical game theory and modeling human adversaries' bounded rationality is critical. To this end, quantal response (QR) has provided very promising results to model human bounded rationality. However, in computing optimal defender strategies in real-world security games against a QR model of attackers, we face difficulties including (1) solving a nonlinear non-convex optimization problem efficiently for massive real-world security games; and (2) addressing constraints on assigning security resources, which adds to the complexity of computing the optimal defender strategy. This paper presents two new algorithms to address these difficulties: Gosaq can compute the globally optimal defender strategy against a QR model of attackers when there are no resource constraints and gives an efficient heuristic otherwise; Pasaq in turn provides an efficient approximation of the optimal defender strategy with or without resource constraints. These two novel algorithms are based on three key ideas: (i) use of a binary search method to solve the fractional optimization problem efficiently, (ii) construction of a convex optimization problem through a non-linear transformation, (iii) building a piecewise linear approximation of the non-linear terms in the problem. Additional contributions of this paper include proofs of approximation bounds, detailed experimental results showing the advantages of Gosaq and Pasaq in solution quality over the benchmark algorithm (Brqr) and the efficiency of Pasaq. Given these results, Pasaq is at the heart of the PROTECT system, which is deployed for the US Coast Guard in the port of Boston, and is now headed to other ports.

Cyber deception has great potential in thwarting cyberattacks [1, 4, 8]. A defender (e.g., network administrator) can use deceptive cyber artifacts such as honeypots and faking services to confuse attackers (e.g., hackers) and thus reduce the success rate and effectiveness of attacks. However, the attackers are often diverse and some attackers may be aware of the deception and adapt to the defender's strategy. Therefore, when the defender designs the deception strategy, he needs to take into account the attacker's strategic response. Game theory is suitable for such strategic interaction between the defender and the attacker. Building upon our previous work on security games,[2, 3] we developed a series of game-theoretic models for cyber deception, as well as algorithms to compute the equilibrium or the optimal defender strategy in the games. The first model we proposed is the Cyber Deception Game, a zero-sum Stackelberg game between the defender and an adversary. In this game, the defender is tasked to protect a set of targets, where each target corresponds to a system or a node in a network. Each target has a true configuration, which consists of a set of attributes, e.g., an operating system, services hosted, etc. The defender can choose an observed configuration for each target when responding to probes and scans that may be launched by the attacker. The observed configuration can be different from the true configuration and thus the deception. The attacker, after collecting information about the observed configuration, chooses which target to attack. We show that finding the defender's optimal strategy is NP-hard and provides mixed-integer linear programming (MILP)-based algorithms to compute the optimal deception scheme, and also several heuristic algorithms. In a follow-up work,[7] we extended this game model to a general-sum one, which captures the fact that the cost for the defender may not always be equal to the gain of the attacker. The general-sum nature of this new game model leads to new computational challenges. We provided a Fully Polynomial Time Approximation Scheme (FPTAS) for solving the game and designed a MILP-based algorithm together with several techniques to speed up the computation. In a recent work,[5] we proposed a new attack graph-based Stackelberg security game model and analyze the optimal deception strategy the defender can use. In contrast to previous models, the attacker in this new game can take sequential actions to reach the targets, which is modeled as taking a path on the attack graph. The defender can strategically manipulate the attack graph through deceptive actions in addition to allocating defensive resources to protect important targets from attackers. We provided a MILP-based solution for a special class of attack graphs and a neural architecture search-based method for general directed acyclic attack graphs. We empirically demonstrated the benefit of deception in all these game models and the scalability of the algorithms. This talk features an introduction to these models and algorithms, together with a discussion on future research directions for game theory-based cyber deception.

Existing passive defence methods cannot effectively guarantee network security; to solve this problem, a novel method is proposed that selects the optimal defence strategy. The network attack-defence process is modelled based on the Bayesian game. The payoff is quantified from the impact value of the attack-defence actions. The optimal defence strategy is selected that takes defence effectiveness as the criterion. The rationality and feasibility of the method are verified through a representative example, and the general rules of network defence are summarised. Compared to the classic strategy selection methods based on game theory, the proposed method can select the optimal strategy in the form of pure strategy by quantifying defence effectiveness, which was proven to perform better.

With the acceleration of the Internet of things (IoT) construction, the security and energy consumption of IoT will become an import factor restricting the overall development of the IoT. In order to reduce the energy consumption of the IoT heterogeneous perceptual network in the attack-defense process, the placement strategy of the intrusion detection system (IDS) described in this paper is to place the IDS on the cluster head nodes selected by the clustering algorithm called ULEACH, which we have proposed in this paper. By optimizing the calculation of the node threshold, the ULEACH clustering algorithm will comprehensively consider the heterogeneity of the perceptual nodes and take the residual energy, energy consumption rate, and overall performance of the nodes into account. As a result, the strategy improves the utilization of the nodes to enhance the performance of heterogeneous perceptual network and extend the lifetime of the system. Furthermore, the paper proposes a intrusion detection system framework and establishes dynamic intrusion detection model for IoT heterogeneous perceptual network based on game theory, by applying modified particle swarm optimization, the optimal defense strategy that could balance the detection efficiency and energy consumption of the system is obtained. Finally, the experiment results show that proposed strategy not only effectively detects multiple network attacks, but also reduces energy consumption.

This paper proposes a two-person multi-stage zero-sum game model considering the confrontation between cascading failures and control strategies in an AC/DC hybrid system to solve the blocking problem of DC systems caused by successive failures at the receiving end of an AC/DC system. A game model is established between an attacker (power grid failure) and a defender (dispatch side). From the attacker’s perspective, this study mainly investigates the problem of system line failures caused by AC or DC blockages. From the perspective of dispatch-side defense, the multiple-feed short-circuit ratio constraint method, output adjustment measures of the energy storage system, sensitivity control, and distance third-segment protection adjustment are used as strategies to reduce system losses. Using as many line return data as possible as samples, the deep Q-network (DQN), a deep reinforcement learning algorithm, is used to obtain the Nash equilibrium of the game model. The corresponding optimal dispatch and defense strategies are also obtained while obtaining the optimal sequence of tripping failures for AC/DC hybrid system cascading failures. Using the improved IEEE 39-node system as an example, the simulation results verify the appropriateness of the two-stage dynamic zero-sum game model to schedule online defense strategies and the effectiveness and superiority of the energy storage system participating in defense adjustment.

Security is a critical concern around the world. In many security domains, limited security resources prevent full security coverage at all times; instead, these limited resources must be scheduled, avoiding schedule predictability, while simultaneously taking into account different target priorities, the responses of the adversaries to the security posture and potential uncertainty over adversary types. Computational game theory can help design such unpredictable security schedules. Indeed, casting the problem as a Bayesian Stackelberg game, we have developed new algorithms that are now deployed over multiple years in multiple applications for security scheduling. These applications are leading to real-world use-inspired research in the emerging research area of “security games” specifically, the research challenges posed by these applications include scaling up security games to large-scale problems, handling significant adversarial uncertainty, dealing with bounded rationality of human adversaries, and other interdisciplinary challenges.

Wireless sensor networks has grown a lot in recent years and offers excellent opportunities in defense related applications such as monitoring environment and collecting data related to antisocial activities. Currently, the needs of wireless sensors have become inevitable in daily life. With the continuous growth of wireless sensor networks in daily life, business, and defense applications, the security of transferring data from sensors to their destination has become an important research area. Due to the limitations of power, storage, and processing capabilities, current security mechanisms of wireless networks or wired networks can not apply directly to wireless sensor networks. So there is a need to develop new techniques or modify the current security mechanisms to transfer data from source (from the field) to base station (destination). In this paper, we discuss currently available intrusion detection techniques, attack models using game theory, and then propose a new framework to detect malicious nodes using zero sum game approach for nodes in the forward data path. The first part of research provides the game model with probability of energy required for transferring the data packets. The second part derives the model to detect the malicious nodes using probability of acknowledgement at source.

Heterogeneous Vehicular Networks (HetVNETs) are an evolution of the traditional Vehicular Networks (VANETs) that integrate long-term and short-term communications technologies to deliver strategic services for roads and smart cities (for example, Intelligent Transport, Safety Notification, Multimedia Content, and others). Despite the recent evolution of HetVNETs, points related to security, data integrity and privacy are still open issues, since there is no standard to achieve trust among the network nodes. A promising approach to achieve trust is the usage of a reputation system, that monitors the behavior of network nodes to determine its trustworthiness. However, this de-termination is complex, because the uncertainty coming from the HetVNETs features, such as fast mobile nodes, self-organization, unstable communication links and frequently changing topology. Within this context, this paper presents a reputation system for HetVNETs, called TrustV, to perform a faster and reliable determination of network nodes trustworthiness based on game theory, as well as the long-term and short-term communication technologies of HetVNETs. The game theory approach allows TrustV to overcome the uncertain issues of HetVNETs, since the modeling of the cooperation as a game can identify selfish and malicious nodes during the time. The results suggest that the TrustV achieves a very high cooperation rate between vehicles at Het VNETs, even as it is capable to detect selfish and malicious nodes as well as black hole and grey hole attacks.

A Stackelberg security Markov game based on partial information for strategic decision making against unexpected attacks

This article considers a class of dynamic single-article facility location problems in which the facility must determine order and inventory levels to meet the dynamic demands of the customers over a finite horizon. The motivating application of this class of problems is in military logistics and the decision makers in this area are not only concerned with the logistical costs of the facility but also with centering the facility among the customers in each time period in order to be able to provide other services. Both the location plan and inventory plan of the facility in the problem must be determined while considering these different metrics associated with the performance of these plans. Effective dynamic programming algorithms for this class of problem are provided for both of these metrics. These dynamic programming algorithms are utilized in order to construct the efficient frontier associated with these two metrics in polynomial time. Computational testing indicates that these algorithms can be used in planning activities for military logistics. [Supplemental materials are available for this article. Go to the publisher’s online edition of IIE Transactions for a worst-case example of constructing the efficient frontier.]

A collection of Ken Binmore's influential papers on bargaining experiments, with the author's newly written commentary addressing the challenges to game theory posed by the behavioral school of economics. This volume brings together all of Ken Binmore's influential experimental papers on bargaining along with newly written commentary in which Binmore discusses the underlying game theory and addresses the criticism leveled at it by behavioral economists. When Binmore began his experimental work in the 1980s, conventional wisdom held that game theory would not work in the laboratory, but Binmore and other pioneers established that game theory can often predict the behavior of experienced players very well in favorable laboratory settings. The case of human bargaining behavior is particularly challenging for game theory. Everyone agrees that human behavior in real-life bargaining situations is governed at least partly by considerations of fairness, but what happens in a laboratory when such fairness considerations supposedly conflict with game-theoretic predictions? Behavioral economists, who emphasize the importance of other-regarding or social preferences, sometimes argue that their findings threaten traditional game theory. Binmore disputes both their interpretations of their findings and their claims about what game theorists think it reasonable to predict. Binmore's findings from two decades of game theory experiments have made a lasting contribution to economics. These papers—some co-authored with other leading economists, including Larry Samuelson, Avner Shaked, and John Sutton—show that game theory does indeed work in favorable laboratory environments, even in the challenging case of bargaining. Does Game Theory Work? The Bargaining Challenge, Volume 2

How to Choose Between Exponentially Many Strategies In “Solving Zero-Sum Games Using Best-Response Oracles with Applications to Search Games,” L. Hellerstein, T. Lidbetter, and D. Pirutinsky consider zero-sum games between a minimizer and a maximizer, where the number of pure strategies of the minimizer is exponential in the number of pure strategies of the maximizer. Such games are frequent in the search-games literature. Solving them with standard algorithms typically takes exponential time. The authors show how to compute (approximate) solutions in polynomial time, provided that there is a polynomial time (approximate) algorithm solving the best-response problem: given a mixed (randomized) strategy of the maximizer, what is a best response of the minimizer? The paper presents both a learning approach using weight updates and an approach of solely theoretical interest based on the ellipsoid algorithm. The learning approach performs well experimentally compared with approaches in the literature. The results are applied to obtain new algorithms for solving specific search games.

Game is a fundamental part of human life and a universal phenomenon. Throughout history, games have played an important role in every age from childhood to adulthood, contributing to personal and social development. However, over time, with the development of industrial processes, games have become a part of the culture industry and this has led to a change in the meaning and function of games. This study emphasises the relationship of the series Squid Game with the game and its changing function. In the theoretical part of the study, the definition of the concept of game and its relationship with culture are evaluated by focusing on Johan Huizinga’s ‘Game Theory’. Then, the traditional children's games in the series Squid Game are analysed in the context of Huizinga's game theory and the function and meaning of the game that has been lost with capitalist life are revealed. In the study, four of the six children's games in the series were analysed using semiotic and discourse analysis methods. These analyses show how the elements of fun, sincerity and enjoyment change under difficult conditions. The findings reveal that economic pressures and social inequalities have transformed the function of games and that children's games have become deadly and cruel in the struggle for survival and the ambition to make money in modern society. As a result, it is understood that children's games in the series have lost their innocence by breaking away from their real ties with the capitalist system.

Use of Lagrange Interpolating Polynomials in RLT. Systems in Series. Parallel Configurations. Spanning trees. Recycling. Split Cuts. Retrial Queues. Horse Racing. MACBETH (Measuring Attractiveness by a Categorical Based Evaluation Technique). Dual simplex. History of CP. Procurement Contracts. MRP. Just in Time. Push and Pull Production. MULTIVARIATE INPUT MODELING. Operational Research Society. The M/G/1 Queue. Drama Theory. Hyper-heuristics. Antithetic Variates. Music. Edgeworth market games. Operational Risk. Queueing Notation. Environmental impact. Why variance is not risk. Conjugate gradient methods. Birth-and-Death Processes. Surgery Planning and Scheduling. Large-scale linear models. Standby Systems. Billiards/Pool. AXIOMATIC MEASURES OF RISK AND RISK-VALUE MODELS. Age Replacement Policies. PASTA and Related Results. Collaborative Procurement. LP projection algorithms. R&D risk management. Bioterrorism. Vendor-Managed Inventory. Gomory Cuts. Travel demand modeling. System Availability. Infinite horizon problems. Trust. Bayesian Network Classifiers. Integer Programming Duality. Queueing Disciplines. Stakeholder participation. Models and Basic Properties. Parametric LP analysis. Operations Research and Golfing. Ice Hockey. Clustering. Finite-Population Models. Batch Arrivals and Service. Optimal Monitoring Strategies. Basic Polyhedral Theory. Operations Research and Art. Managing portfolios of risks. Measures of risk equity. Branch and Bound Algorithms. Analytics in Retail. Variational Inequalities. Cross-Entropy Method. TSP heuristics. Capacity allocation. Control Variates. Cover Inequalities. Direct Search Methods. NLP Software. Bilinear optimization. Discrete-Time Martingales. Branch and Price. Column generation. IP Preprocessing. Optimal Reliability Allocation. Simplex-based LP solvers. Instance Formats. Clique generalizations. Availability in Stochastic Models. Evolutionary Algorithms. Basic CP theory: Search. Multimethodology. Branch and Cut. Memetic Algorithms. Urban mass transit. Coherent Systems. Subgradient optimization. Klimov's Model. Credit risk assessment. Branch-width and Tangles. Stochastic Hazard Process. Deterministic Global Optimization. Multistage (stochastic) games. Basis Reduction Methods. ACCIDENT PRECURSORS AND WARNING SYSTEMS MANAGEMENT: A BAYESIAN APPROACH TO MATHEMATICAL MODELS. Genetic Algorithms. Newsvendor models. Parallel Systems. Opportunities in Tennis. Job-shop scheduling. Simplex method and complexity. Geometric programming. Evacuation Planning. The Performance? Allocation Games. Scenario Generation. Robustness Analysis. Dynamic auctions. Neuroeconomics and game theory. Common Random Numbers. Selective Support Vector Machines. Semi-Markov Processes. Risk Averse Models. Sampling Methods. Domination problems. Simulation of Rare Events. Introduction to Tabu Search. Basic Interdiction Models. Introductory Concepts. Remanufacturing. PAUSE procedure. Wardrop equilibria. Mass customization. Ellipsoidal algorithms. Benders decomposition. Dynamic vehicle routing. Warranty Modeling. Design for Network Resiliency. Passenger rail transportation. Vehicle Routing Problem. Supply Chain Outsourcing. Lovasz-Schriver Reformulation. Hazard Rate Function. Lot-sizing. Differential games. Regenerative Processes. Guided Local Search. Preferences for repeated gambles. Percolation Theory. Simulated Annealing. Scoring Rules. Mixing Sets. Cricket. Prospect Theory. Combining Forecasts. Graph search techniques. Quasi-Newton methods. Random search algorithms. Supply chain coordination. Service Outsourcing. Markov Renewal Processes. K-out-of-n Systems.

This study investigates the application of game-based learning in game theory education, presenting a comprehensive framework for implementing gamification strategies through a "Market Gaming Simulation" approach. Traditional game theory instruction often struggles to effectively convey complex theoretical concepts and bridge the gap between abstract mathematics and practical applications. To address these challenges, this research develops and analyzes a strategic simulation game that integrates core game theory concepts—including Nash equilibrium, Prisoner's Dilemma, dynamic game analysis, mixed-strategy equilibrium, and Bayesian games—into an interactive learning environment.The study first examines the current limitations in game theory pedagogy and establishes the theoretical foundation for game-based learning implementation. It then presents a detailed design of the "Market Gaming Simulation," including game objectives, core mechanisms, role design, and operational processes. The simulation creates a competitive marketplace where students, through multiple rounds of decision-making, experience the practical applications of game theory concepts while developing strategic thinking capabilities.A systematic implementation framework is proposed, encompassing pre-course preparation, classroom execution, and post-game analysis. The framework includes comprehensive guidelines for both instructors and students, real-time feedback mechanisms, and evaluation systems. Results indicate that this gamified approach significantly enhances student engagement, improves understanding of theoretical concepts, and develops practical decision-making skills. While acknowledging implementation challenges such as the need for elevated pedagogical expertise and flexible game design, this study provides valuable insights for integrating game-based learning into game theory education, contributing to the advancement of interactive and effective teaching methodologies in higher education.